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transfer learning | Is convergence to a local minima more likely with transfer learning? | https://ai.stackexchange.com/questions/13567/is-convergence-to-a-local-minima-more-likely-with-transfer-learning | <p>While doing transfer learning where my two problems are face-generation and car-generation is it likely that, if I use the weights of one problem as the initialization of the weights for the other problem, the model will converge to a local minima? In any problem is it better to train from scratch over transfer lear... | 412 | |
transfer learning | What is the difference between using a backbone architecture and transfer learning? | https://ai.stackexchange.com/questions/17750/what-is-the-difference-between-using-a-backbone-architecture-and-transfer-learni | <p>I'm super new to deep learning and computer vision, so this question may sound dumb.</p>
<p>In this link (<a href="https://github.com/GeorgeSeif/Semantic-Segmentation-Suite" rel="nofollow noreferrer">https://github.com/GeorgeSeif/Semantic-Segmentation-Suite</a>), there are pre-trained models (e.g., ResNet101) calle... | 413 | |
transfer learning | Transfer learning from model trained in a similar dataset | https://ai.stackexchange.com/questions/5084/transfer-learning-from-model-trained-in-a-similar-dataset | <p>I am currently working on a defect detection algorithm but I only have a few samples of defects.I googled for defect detection datasets and I found this one: </p>
<p><a href="http://resources.mpi-inf.mpg.de/conferences/dagm/2007/prizes.html" rel="nofollow noreferrer">http://resources.mpi-inf.mpg.de/conferences/dagm... | 414 | |
transfer learning | How is transfer learning used to mitigate catastrophic forgetting in neural networks? | https://ai.stackexchange.com/questions/14117/how-is-transfer-learning-used-to-mitigate-catastrophic-forgetting-in-neural-netw | <p>How can transfer learning be used to mitigate <a href="https://en.wikipedia.org/wiki/Catastrophic_interference" rel="nofollow noreferrer">catastrophic forgetting</a>. Could someone elaborate on this?</p>
| <p>Transfer learning is a field where you apply knowledge from a source onto a target. This is a vague notion and there is an abundance of literature pertaining to it. Given your question I will work under the assumption that you are referring weight/architecture sharing between model (in other words training a model o... | 415 |
transfer learning | What is the difference between one-shot learning, transfer learning and fine tuning? | https://ai.stackexchange.com/questions/21719/what-is-the-difference-between-one-shot-learning-transfer-learning-and-fine-tun | <p>Lately, there are lots of posts on <em>one-shot</em> learning. I tried to figure out what it is by reading some articles. To me, it looks like similar to <em>transfer</em> learning, in which we can use pre-trained model weights to create our own model. <em>Fine-tuning</em> also seems a similar concept to me. </p>
<... | <p>They are all related terms.</p>
<p>From top to bottom:</p>
<p><strong>One-shot learning</strong> aims to achieve results with one or very few examples. Imagine an image classification task. You may show an apple and a knife to a human and no further examples are needed to continue classifying. That would be the id... | 416 |
transfer learning | Keep weights of output layer in transfer learning? | https://ai.stackexchange.com/questions/38033/keep-weights-of-output-layer-in-transfer-learning | <p>I'm seeing conflicting info on what to do with the fully-connected output layer of a pre-trained network when it's used in transfer learning. <a href="https://datascience.stackexchange.com/questions/76370/how-many-layers-should-i-replace-in-transfer-learning-cnn">A previous answer</a> seems to imply that the network... | <p>Transfer learning is a complete field of research, and there are multiple possibilities for what might work best in each situation.</p>
<p>There are various ways in which you can employ a pretrained model for transfer learning. You can indeed keep the complete model intact, but it is more common (as is done in pytor... | 417 |
transfer learning | Is transfer learning effective when the new task has more classes than the original? | https://ai.stackexchange.com/questions/38030/is-transfer-learning-effective-when-the-new-task-has-more-classes-than-the-origi | <p>All examples of transfer learning I have seen for classification use initial weights of a network trained on a larger number of classes (say 1000 in the case of networks trained on ImageNet data) to address a new task that has a smaller number of classes. Can transfer learning be effectively used when the new task h... | <p>Yes, transfer learning can usually also be utilized when the number of classes differs from the original. Your model will however be more 'transferable' if it has been trained on a wide variety of data or data that is somewhat similar to your new data.
What one would usually do is freeze the weights of the lower lay... | 418 |
transfer learning | Why people don't use Supervised Learning in Transfer Learning for Reinforcement Learning? | https://ai.stackexchange.com/questions/47888/why-people-dont-use-supervised-learning-in-transfer-learning-for-reinforcement | <p>I am reading some books and papers on Transfer Learning for Reinforcement Learning and have some questions.</p>
<p>Suppose previous MDPs and target MDP share the same state space, action space and transition function, but differ in their reward functions, parameterized as <span class="math-container">$r_{\theta}(s,a... | <p>If the target MDP is significantly different in terms of reward structure, the policy learned from past MDPs may not generalize well intuitively. The dataset <span class="math-container">$(\theta_{i},s,\pi_{\theta_{i}}(s))$</span> only contains states visited by past policies. The new policy may visit new states not... | 419 |
transfer learning | learning object recognition of primitive shapes through transfer learning problem | https://ai.stackexchange.com/questions/16716/learning-object-recognition-of-primitive-shapes-through-transfer-learning-proble | <p>Question on transfer learning object classification (MobileNet_v2 with 75% number of parameters) with my own synthetic data:</p>
<p>I made my own dataset of three shapes: triangles, rectangles and spheres. each category has 460 samples with diferent sizes, dimensions, different wobbles at edges. They look like this... | 420 | |
transfer learning | What is the difference between feature extraction and fine-tuning in transfer learning? | https://ai.stackexchange.com/questions/28138/what-is-the-difference-between-feature-extraction-and-fine-tuning-in-transfer-le | <p>I'm building a model for <strong>facial expression recognition</strong>, and I want to use <em><strong>transfer learning</strong></em>. From what I understand, there are different steps to do it. The first is the <strong>feature extraction</strong> and the second is <strong>fine-tuning</strong>. I want to understand... | <p>It has been a while since the question was asked, but I came up with this <a href="https://developer.baidu.com/article/details/2727266" rel="nofollow noreferrer">article</a>. It helped me to understand the topic. From the article:</p>
<p><strong>Feature-based</strong> methods involve using the intermediate represent... | 421 |
transfer learning | Should you reload the optimizer for transfer learning? | https://ai.stackexchange.com/questions/10545/should-you-reload-the-optimizer-for-transfer-learning | <p>For example, you train on dataset 1 with an adaptive optimizer like Adam. Should you reload the learning schedule, etc., from the end of training on dataset 1 when attempting transfer to dataset 2? Why or why not?</p>
| <p>When doing transfer learning it makes sense to have different update policies for "inherited" parameters and the "new" parameters. "Inherited" parameters are pre-trained on dataset1 and they typically form the front end of the deep model. The "new" parameters are trained from scratch and they typically produce the d... | 422 |
transfer learning | What is the difference between "out-of-distribution (generalisation)" and "(meta)-transfer learning"? | https://ai.stackexchange.com/questions/18754/what-is-the-difference-between-out-of-distribution-generalisation-and-meta | <p>I'm trying to develop a better understanding of the concept of "out-of-distribution" (generalization) in the context of Bengio's "Moving from System 1 DL to System 2 DL" and the concept of "(meta)-transfer learning" in general. </p>
<p>These concepts seem to be very strongly related, maybe even almost referring to ... | 423 | |
transfer learning | How to choose the new layer and objective function for transfer learning on a neural network? | https://ai.stackexchange.com/questions/32171/how-to-choose-the-new-layer-and-objective-function-for-transfer-learning-on-a-ne | <p>I have a base model <span class="math-container">$M$</span> trained on a data say type 1 for task <span class="math-container">$T$</span>. Now, I want to update <span class="math-container">$M$</span> by applying transfer learning for it to work on data type 2 for the same task <span class="math-container">$T$</span... | 424 | |
transfer learning | How to Apply a Pre-trained Jigsaw Puzzle Model for Transfer Learning on Larger Images? | https://ai.stackexchange.com/questions/46590/how-to-apply-a-pre-trained-jigsaw-puzzle-model-for-transfer-learning-on-larger-i | <p>I’ve read the article titled <strong><a href="https://arxiv.org/abs/1603.09246" rel="nofollow noreferrer">Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles</a></strong>.</p>
<p>In this article, the authors create jigsaw puzzles and train a model to solve them. The process involves starting wi... | 425 | |
transfer learning | Transfer learning to train only for a new class while not affecting the predictions of the other class | https://ai.stackexchange.com/questions/14147/transfer-learning-to-train-only-for-a-new-class-while-not-affecting-the-predicti | <p>I am basically interested in vehicle on the road. </p>
<p><a href="https://github.com/ayooshkathuria/pytorch-yolo-v3" rel="nofollow noreferrer">YoloV3 pytorch</a> is giving a decent result.</p>
<p>So my interested Vehicles <code>Car</code> <code>Motorbike</code> <code>Bicycle</code> <code>Truck</code> and <code>b... | <p>Even if you want to re-train your model for just one new class you will have to prepare your training data such that it includes all or most of the classes which you want to predict. Most of the times last two layers of a network have the data of number of labels which are to be predicted and that should always be s... | 426 |
transfer learning | Is it ok to perform transfer learning with a base model for face recognition to perform one-shot learning for object classification? | https://ai.stackexchange.com/questions/23897/is-it-ok-to-perform-transfer-learning-with-a-base-model-for-face-recognition-to | <p>I am trying to create a model that is using a <em>one-shot learning</em> approach for a classification task. We do this because we do not have a lot of data and it also seems like a good way to learn this approach (it is going to be a university project). The task would be to classify objects, probably from drone/sa... | 427 | |
transfer learning | When doing transfer learning, which initial layers do we need to freeze, and how should I change the last layer for my task? | https://ai.stackexchange.com/questions/5370/when-doing-transfer-learning-which-initial-layers-do-we-need-to-freeze-and-how | <p>I want to train a neural network for the detection of a single class, but I will be extending it to detect more classes. To solve this task, I selected the PyTorch framework.</p>
<p>I came across <a href="http://cs231n.github.io/transfer-learning/" rel="nofollow noreferrer">transfer learning</a>, where we fine-tune ... | <p>After a lot of browsing online for answers to these questions, this is what I came up with.</p>
<blockquote>
<ol>
<li>What are the initial layers in this case? How exactly can I freeze them?</li>
</ol>
</blockquote>
<p>The <strong>initial few layers</strong> are said to extract the most general features of any kind ... | 428 |
transfer learning | Transfer Learning for Solar Energy Production Forecasting with LSTM: Generalized vs. Specialized Models | https://ai.stackexchange.com/questions/39888/transfer-learning-for-solar-energy-production-forecasting-with-lstm-generalized | <p>I am working on a solar energy production forecasting problem using LSTM multi-step models to predict 1/4/8h ahead of solar energy production for different solar installations. Our goal is to help clients optimize their energy utilization by trading with their neighbours or respective Microgrids.</p>
<p>I have clust... | <p>I would probably refrain from using transfer learning. Transfer learning works if you have a more specific data <strong>set</strong>. Transfer learning on a single sample is going to be very hard, and I'm unfamiliar with any papers that attempt such a feat.</p>
<p>Model conditioning is when you add variables as 'con... | 429 |
transfer learning | In transfer learning, should we apply standardization if the pre-trained model was (or not) trained with standardised data? | https://ai.stackexchange.com/questions/8744/in-transfer-learning-should-we-apply-standardization-if-the-pre-trained-model-w | <p>Assume one is using transfer learning via a model which was trained on ImageNet.</p>
<ol>
<li><p>Assume that the pre-processing, which was used to achieve the pre-trained model, contained z-score standardization using some mean and std, which was calculated on the training data.</p>
<p>Should one apply the same tran... | <ol>
<li>You should use the same transformation (i.e same mean and std in case of z-score normalization) on the new data while using any pretrained model.</li>
<li>If the pretrained model was trained without any normalization then it does not matter if you use normalization or not as long as range of input data is fixe... | 430 |
transfer learning | Transfer learning using pretrained tensorflow object detection model | https://ai.stackexchange.com/questions/42473/transfer-learning-using-pretrained-tensorflow-object-detection-model | <p>I am new to AI/ML and wanted to seek guidance as I am totally lost. I will simplify my issue as follows:</p>
<p>Let's say I would like to detect apples and oranges in images.
I would like to leverage a pre-trained Faster RCNN model for efficiency.
My understanding is that I need to remove the last two layers of such... | <p>The Faster R-CNN from the TensorFlow Hub is an object detection model, not just a classification model. That explains why it has 8 outputs, including softmax probabilities and bounding boxes. If you only want the probabilities, I suggest the following code :</p>
<pre><code>n_classes = 2
model_handle = "https:/... | 431 |
transfer learning | what is `Normalize` for in PyTorch transfer learning tutorial? | https://ai.stackexchange.com/questions/36396/what-is-normalize-for-in-pytorch-transfer-learning-tutorial | <p>in <a href="https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html" rel="nofollow noreferrer">this pytorch tutorial</a>, there is <code>transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])</code>, what is the purpose of this?</p>
<p>(i removed it and the code still works)</p>
| <p>Those are mean and standard deviation used to standardize each channel of the images from <a href="https://www.image-net.org/" rel="nofollow noreferrer">IMAGENET</a> used to train the <a href="https://pytorch.org/vision/stable/models.html" rel="nofollow noreferrer">torchvision pretrained models</a>.</p>
<p>Since the... | 432 |
transfer learning | Transferring a Q-learning policy to larger instances | https://ai.stackexchange.com/questions/32041/transferring-a-q-learning-policy-to-larger-instances | <p><strong>How do I best transfer and fine-tune a Q-learning policy that was trained on small instances to large instances?</strong></p>
<p><em>Some more details on the problem:</em>
I am currently trying to derive a decision policy for a dynamic vehicle dispatching problem.
In the problem, a decision point occurs when... | 433 | |
transfer learning | Does layer freezing offer other benefits other than to reduce computational time in gradient descent? | https://ai.stackexchange.com/questions/39239/does-layer-freezing-offer-other-benefits-other-than-to-reduce-computational-time | <p>In Deep Learning and Transfer Learning, does layer freezing offer other benefits other than to reduce computational time in gradient descent?</p>
<p>Assuming I train a neural network on task A to derive weights <span class="math-container">$W_{A}$</span>, set these as initial weights and train on another task B (wit... | <p>from your post I assume you are having three sub-questions and I will answer them one by one.</p>
<ul>
<li>For the 1st question, yes, layer freezing reduces the computational cost a lot and also helps the model to keep the many patterns it has learned to recognize things better, c negative to positive samples easier... | 434 |
transfer learning | Precise description of one-shot learning | https://ai.stackexchange.com/questions/16138/precise-description-of-one-shot-learning | <p>I am working on classifying the <a href="https://github.com/brendenlake/omniglot" rel="nofollow noreferrer">Omniglot dataset</a>, and the different papers dealing with this topic describe the problem as <em>one-shot learning</em> (classification). I would like to nail down a precise description of what counts as <em... | <p>The model has learnt the "features" for the type of inputs, eg. faces.
For the problem to be called one-shot, it needs to also correctly classify/compare any new samples. For example, in face recognition application, any new person's images should also result a positive for their own image and negative for any othe... | 435 |
transfer learning | how to determine the number of units for dense layer for transfer learning? | https://ai.stackexchange.com/questions/42877/how-to-determine-the-number-of-units-for-dense-layer-for-transfer-learning | <p>I'm using MobileNetV2 for classification, and I want to add dense layers(i remove the last layer of the MobileNetV2 model). How do I choose the number of units for the dense layer after obtaining the feature vector (1280)? Is there a formula to determine the units for each dense layer I add?</p>
| <p>Not only the units but also the number of layers... you can reason over something like "how complex is your task", but usually we resort to grid search over some educated guesses (like 2/3 layer of 128/256 neurons, but depends)</p>
| 436 |
transfer learning | How to train my model using transfer learning on inception_v3 pre-trained model? | https://ai.stackexchange.com/questions/27418/how-to-train-my-model-using-transfer-learning-on-inception-v3-pre-trained-model | <p>I am trying to train my model to classify 10 classes of hand gestures but I don't get why am I getting validation accuracy approx. double than training accuracy.</p>
<p>My dataset is from kaggle:<br />
<a href="https://www.kaggle.com/gti-upm/leapgestrecog/version/1" rel="nofollow noreferrer">https://www.kaggle.com/g... | <p>The problem is that you're not creating a model with <code>InceptionV3</code> as the backbone. What you want to do is this:</p>
<pre><code>input_tensor = Input(shape=(240,320,1) )
base_model = tf.keras.applications.InceptionV3(input_tensor=Input(shape=(240,320,3)),
include_top=False, weights='imagenet')
ba... | 437 |
transfer learning | Why do the authors of the T5 paper say that the "architectural changes are orthogonal to the experimental factors"? | https://ai.stackexchange.com/questions/31689/why-do-the-authors-of-the-t5-paper-say-that-the-architectural-changes-are-ortho | <p>Here's a quote from the <code>T5 paper</code> (T5 stands for "Text-to-Text Transfer Transformer") titled <a href="https://www.jmlr.org/papers/volume21/20-074/20-074.pdf" rel="nofollow noreferrer">Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer</a> by <em>Colin Raffel et al... | <p>"Orthogonal" is often used to mean "independent", as in "independent variable which does not correlate with the other variables". I believe this terminology originates from <a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow noreferrer">principal component... | 438 |
transfer learning | Can an image recognition model used for human pose estimation? | https://ai.stackexchange.com/questions/17261/can-an-image-recognition-model-used-for-human-pose-estimation | <p>I am currently writing my thesis about human pose estimation and wanted to use Google's inception network, modify it for my needs and use transfer learning to detect human key joints. I wanted to ask if that could be done in that way?</p>
<p>Assuming I am having n-keypoints, generating the n-feature maps, use trans... | 439 | |
transfer learning | Are artificial intelligence learnings or trainings transferable from one agent to the other? | https://ai.stackexchange.com/questions/8920/are-artificial-intelligence-learnings-or-trainings-transferable-from-one-agent-t | <p>One disadvantage or weakness of Artificial Intelligence today the slow nature of learning or training success. For instance, an AI agent might require a 100,000 samples or more to reach an appreciable level of performance with a specific task. But this is unlike humans who are able to learn very quickly with a minim... | <p>The simplest case is copying the software. That is instant duplication of the learnings. In a similar, but less trivial way you can adjust pretrained neural network classifiers to new datasets by simply re-initializing the last layer.</p>
<p>It gets more interesting when you have multiple agents and you want to com... | 440 |
transfer learning | What does "semantic gap" mean? | https://ai.stackexchange.com/questions/27382/what-does-semantic-gap-mean | <p>I was reading <a href="https://www.sciencedirect.com/science/article/abs/pii/S0925231220300874" rel="noreferrer">DT-LET: Deep transfer learning by exploring where to transfer</a>, and it contains the following:</p>
<blockquote>
<p>It should be noted direct use of labeled source domain data on a new scene of target d... | <p>In terms of transfer learning, semantic gap means different meanings and purposes behind the same syntax between two or more domains. For example, suppose that we have a deep learning application to detect and label a sequence of actions/words <span class="math-container">$a_1, a_2, \ldots, a_n$</span> in a video/te... | 441 |
transfer learning | Training a reinforcement learning model with multiple images | https://ai.stackexchange.com/questions/12569/training-a-reinforcement-learning-model-with-multiple-images | <p>I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (e.g., a different "maze").</p>
<p>Suppose I have about <span class="math-container">$10K$</span> different maze images, and the ideal case is that after training <span class="m... | 442 | |
transfer learning | Can we use Autoencoders for unsupervised CNN feature learning? | https://ai.stackexchange.com/questions/12244/can-we-use-autoencoders-for-unsupervised-cnn-feature-learning | <p>I searched through the internet but couldn't find a reliable article that answers this question.</p>
<p>Can we use Autoencoders for unsupervised CNN feature learning of unlabeled images like the below
<a href="https://i.sstatic.net/JC9DO.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/JC9DO.png" alt="... | 443 | |
transfer learning | Is analogy necessary to artificial general intelligence? | https://ai.stackexchange.com/questions/3665/is-analogy-necessary-to-artificial-general-intelligence | <p>Analogies are quite powerful in communication. They allow explaining complex concepts to people with no domain knowledge, just by mapping to a known domain. Hofstadter <a href="https://cogsci.indiana.edu/" rel="nofollow noreferrer">says they matter</a>, whereas Dijkstra says they are dangerous. Anyway, analogies can... | <p>I don't think I can give you a true answer to the actual question as posed, as I don't have a strict definition of "general intelligence". Nor do I have a solid definition of "critical" in context.</p>
<p>However, if we lean on our naive/intuitive understanding of what "general intelligence&... | 444 |
transfer learning | Why aren't the BERT layers frozen during fine-tuning tasks? | https://ai.stackexchange.com/questions/23884/why-arent-the-bert-layers-frozen-during-fine-tuning-tasks | <p>During transfer learning in computer vision, I've seen that the layers of the base model are frozen if the images aren't too different from the model on which the base model is trained on.</p>
<p>However, on the NLP side, I see that the layers of the BERT model aren't ever frozen. What is the reason for this?</p>
| <p>Corrections and other answers are welcome, but here are a few thoughts:</p>
<p>There are several approaches in terms of which weights get frozen (and also other considerations, see for example Fig. 5 in <a href="https://arxiv.org/abs/2211.09085v1" rel="nofollow noreferrer">"Galactica: A Large Language Model for... | 445 |
transfer learning | Why is domain adaptation and generative modelling for knowledge graphs still not applied widely in enterprise data? What are the challenges? | https://ai.stackexchange.com/questions/23396/why-is-domain-adaptation-and-generative-modelling-for-knowledge-graphs-still-not | <p>I see that domain adaptation and transfer learning has been widely adopted in image classification and semantic segmentation analysis. But it's still lacking in providing solutions to enterprise data, for example, solving problems related to business processes?</p>
<p>I want to know what characteristics of the data ... | 446 | |
transfer learning | What is a Hebbian linear classifier? | https://ai.stackexchange.com/questions/27385/what-is-a-hebbian-linear-classifier | <p>I was reading <a href="https://www.researchgate.net/publication/228618750_Deep_learning_of_representations_for_unsupervised_and_transfer_learning" rel="nofollow noreferrer">Deep Learning of Representations for Unsupervised and Transfer Learning</a>,
and they state the following:</p>
<blockquote>
<p>They have only a ... | 447 | |
transfer learning | Are custom tokens better than punctuation pseudo-tokens for LLMs? | https://ai.stackexchange.com/questions/37526/are-custom-tokens-better-than-punctuation-pseudo-tokens-for-llms | <p>I've seen two approaches for introducing custom tokens for transfer learning with large language models like Bert or GPT3. Some approaches introduce new tokens into the vocabulary and learn embeddings from scratch. This is the "traditional" approach. However, I've seen other papers that imitate custom toke... | 448 | |
transfer learning | Is the high dimensionality of input vectors a problem for a radial basis function neural network? | https://ai.stackexchange.com/questions/21030/is-the-high-dimensionality-of-input-vectors-a-problem-for-a-radial-basis-functio | <p>I have a dataset A of videos. I've extracted the feature vector of each video (with a convolutional neural network, via transfer learning) creating a dataset B. Now, every vector of the dataset B has a high dimension (about 16000), and I would like to classify these vectors using an RBF-ANN (there are only 2 possibl... | 449 | |
generative models | Book(s) on generative models | https://ai.stackexchange.com/questions/28521/books-on-generative-models | <p>Generative models in artificial intelligence span from simple models like Naive Bayes to the advanced deep generative models like current day GANs. This question is not about coding and involves only science and theoretical part only.</p>
<p>Are there any standard textbooks that covers topics from scratch to the adv... | <p>From the theoretical foundations one can look into the <code>Chapter 20: Deep Generative Models</code> of the classic DL book by <strong>Goodfellow, Bengio</strong> <a href="https://amzn.to/2MmZNbH" rel="nofollow noreferrer">https://amzn.to/2MmZNbH</a>. Not the most recent reference, but written by the professionals... | 450 |
generative models | What makes ChatGPT a generative model? | https://ai.stackexchange.com/questions/39012/what-makes-chatgpt-a-generative-model | <p>I'm working my way through how ChatGPT works. So I read that ChatGPT is a generative model. When searching for generative models, I found two defintions:</p>
<ul>
<li><a href="https://developers.google.com/machine-learning/gan/generative" rel="nofollow noreferrer">A <strong>generative</strong> model includes the dis... | <h3>What are generative (and discriminative) models?</h3>
<p>If the model learns a distribution of the form <span class="math-container">$p(x)$</span> or <span class="math-container">$p(x, y)$</span>, where <span class="math-container">$x$</span> are the inputs and <span class="math-container">$y$</span> the outputs/la... | 451 |
generative models | Do generative models with 64-bit seeds exist? | https://ai.stackexchange.com/questions/47776/do-generative-models-with-64-bit-seeds-exist | <p>All of the generative models that I have found appear to use 32-bit seeds, at least on my testing.</p>
| <p>32-bit seeds are used due to historical and compatibility reasons, and they provide <span class="math-container">$2^{32}$</span> over 4 billion initial states which is sufficient for most applications including ML since as you may already know that the quality of a PRGN is not most relevant factors for the quality o... | 452 |
generative models | Parametric vs Non-parametric generative models | https://ai.stackexchange.com/questions/36308/parametric-vs-non-parametric-generative-models | <p>I have a little perplexity trying to distinguish <em>parametric</em> vs <em>non-parametric</em> generative model.</p>
<p>In my understanding, a <strong>parametric</strong> generative model would try to learn the probability density function by estimating the parameters of an underlying distribution we are assuming. ... | <p>I call this difference <strong>prescribed</strong> vs <strong>implicit</strong> models.</p>
<p>To my knowledge, a <strong>parametric</strong> model has a fixed,finite number of parameters with respect to the sample size (e.g a linear model), while <strong>nonparametric</strong> model have an increasing number of par... | 453 |
generative models | Is plain autoencoder a generative model? | https://ai.stackexchange.com/questions/36118/is-plain-autoencoder-a-generative-model | <p>I am wondering how a plain auto encoder is a generative model though its version might be but how can a plain auto encoder can be generative. I know that Vaes which is a version of the autoencoder is generative as it generates distribution for latent variables and whole data explicitly. But I am not able to think ho... | <p>An autoencoder is not considered a generative model, because it only reconstructs the given input. You could use the decoder <em>like</em> a generative model by putting in different vectors. However, the standard autoencoder mostly learns a sparse latent space. This means that you will have distinct clusters in the ... | 454 |
generative models | Are generative models actually used in practice for industrial drug design? | https://ai.stackexchange.com/questions/27720/are-generative-models-actually-used-in-practice-for-industrial-drug-design | <p>I just finished reading this paper <a href="https://arxiv.org/abs/2006.10137" rel="nofollow noreferrer">MoFlow: An Invertible Flow Model for Generating Molecular Graphs</a>.</p>
<p>The paper, which is about generating molecular graphs with certain chemical properties improved the SOTA at the time of writing by a bit... | 455 | |
generative models | Can we use MCTS without a generative model? | https://ai.stackexchange.com/questions/3290/can-we-use-mcts-without-a-generative-model | <p>From what I have understood reading the UCT paper <a href="http://ggp.stanford.edu/readings/uct.pdf" rel="nofollow noreferrer">Bandit based monte-carlo planning</a>, by Levente Kocsis and Csaba Szepesvári, MCTS/UCT requires a generative model. </p>
<ol>
<li><p>Does it mean that, in case there is no generative model... | <p>You either need a generative model or an emulator of the environment. In the later case you don't calculate your transitions and rewards using the model but feed your actions and states to the emulator and work with the results.</p>
<p>The emulator can be a black box as long as it returns the next state and the rew... | 456 |
generative models | Modern graduate-level machine learning books with focus on generative models | https://ai.stackexchange.com/questions/41439/modern-graduate-level-machine-learning-books-with-focus-on-generative-models | <p>I'm looking for a modern machine learning book with graduate-level treatment of more recent topics such as diffusion and generative models, transformers etc.</p>
<p>I have a hard copy of <em>Deep Learning</em> by Goodfellow and Bengio; while I liked the book and read it extensively when it was published, it is a bit... | <p><a href="https://sites.google.com/view/berkeley-cs294-158-sp20/home" rel="nofollow noreferrer">Berkeley CS294-158</a> is a graduate-level course on deep unsupervised learning. They cover a lot of architectures used in modern generative modeling. They have recorded lectures and slides online.</p>
<p><a href="https://... | 457 |
generative models | Is the quality of generative model outputs affected by the quality of their PRNGs? | https://ai.stackexchange.com/questions/47745/is-the-quality-of-generative-model-outputs-affected-by-the-quality-of-their-prng | <p>There are newer PRNGs like xoshiro/xoroshiro that have better quality than Mersenne Twister or WELL in a way that they pass statistical tests. Is the use of a better PRNG algorithm crucial to improve the output of generative models? (Anyway I couldn't find any information about what PRNGs are used in popular generat... | <p>Most popular generative models like GANs, VAEs and DDPMs rely on <a href="https://en.wikipedia.org/wiki/Pseudorandom_number_generator" rel="nofollow noreferrer">PRNGs</a> for initializing model weights, sampling latent variables from Gaussian distribution, and some other stochastic operations during training such as... | 458 |
generative models | How to tell if a model is generative vs. predictive? | https://ai.stackexchange.com/questions/46020/how-to-tell-if-a-model-is-generative-vs-predictive | <p>How does one tell if a given model is generative AI or predictive AI?</p>
<p>Do generative models have more outputs than inputs and <em>vice versa</em> for predictive models?</p>
| <p>It's hard nowadays to draw a distinct line between the two with the advent of conditional generation and normalizing flow</p>
<p>Usually, we say that is generative if you try to model the joint probability <span class="math-container">$p(x,y)$</span>, whereas discriminative models try to model the marginal <span cla... | 459 |
generative models | Maximize a scoring function within the latent space of a generative model | https://ai.stackexchange.com/questions/41608/maximize-a-scoring-function-within-the-latent-space-of-a-generative-model | <p>Given a generative model, G, trained on a dataset D. This generative model can be either GAN or Diffusion based. Supposed each sample, x_i, generated by G, can be evaluated by a readily available scoring function, S(x_i).</p>
<p>What are the possible ways to navigate the latent space of G to find generated samples w... | <p>For the first suggestion, are you suggesting doing gradient descent with objective <span class="math-container">$-S$</span>? If so (and <span class="math-container">$S$</span> is differentiable), that's definitely possible. I would suggest looking at the <a href="https://arxiv.org/pdf/2003.03808.pdf" rel="nofollow n... | 460 |
generative models | Why is generative QA + RAG so popular when extractive QA models exist that can provide factual information? | https://ai.stackexchange.com/questions/43646/why-is-generative-qa-rag-so-popular-when-extractive-qa-models-exist-that-can-p | <p>If the concern with using generative models for question answering is that these models aren't always producing factual information, why is it that people are using these models with Retrieval Augmented Generation (Open Generative QA/Closed Generative QA), rather than using a transformers-based extractive QA model t... | <p>The issue is that extractive QA limits the types of questions to ones that have answers that appear <em>exactly</em> in the text.</p>
<p>For example, consider the question:</p>
<pre><code>Is Bob considered a good movie director?
</code></pre>
<p>It's very unlikely that you're going to have a document that says exact... | 461 |
generative models | What's a good generative model for creating valid formats of a person's name? | https://ai.stackexchange.com/questions/15978/whats-a-good-generative-model-for-creating-valid-formats-of-a-persons-name | <p>I'm trying to come up with a generative model that can input a name and output all valid formats of it. </p>
<p>For example, "Bob Dylan" could be an input and the gen model will output "Dylan, Bob", "B Dylan", "Bob D" and any other type of valid formatting of a person's name. So given my example the gen model doe... | 462 | |
generative models | What is x, y, p(x), p(y) in generative model domain? | https://ai.stackexchange.com/questions/41818/what-is-x-y-px-py-in-generative-model-domain | <h3>Background</h3>
<p><strong>Generative modeling</strong> <br>
Generative modeling aims to model the probability of observing an observation <code>x</code>.
<span class="math-container">$$
p(x) = \frac{p(y\cap x)}{p(y|x)}
$$</span></p>
<p><strong>Representation Learning</strong></p>
<p>Instead of trying to model the ... | <p>"<em>What is: x, y, P(x), P(y), P(x|y), P(y|x) in this example?</em>" I don't really see where you are referring to them in the question, so I'll answer with the general interpretation in ML:</p>
<ul>
<li><span class="math-container">$P(x)$</span>: also known as data distribution, it's the density that des... | 463 |
generative models | Blurring of image in generative model using diffusion probabilistic method | https://ai.stackexchange.com/questions/40021/blurring-of-image-in-generative-model-using-diffusion-probabilistic-method | <p>In the 2015 paper "<a href="https://arxiv.org/pdf/1503.03585.pdf" rel="nofollow noreferrer">Deep Unsupervised Learning using Nonequilibrium Thermodynamics</a>" by Sohl-Dickstein et al. on diffusion for generative models, Figure 1 shows the forward trajectory for a 2-d swiss-roll image using Gaussian diffus... | <blockquote>
<p>Figure 1. The proposed modeling framework trained on 2-d swiss roll data</p>
</blockquote>
<p>The data is actually 2d coordinates, plotted on a scatter-plot. It doesn't represent an <span class="math-container">$n \times n$</span> image with individual pixels. And the added noise is 2d, well 1d to the x... | 464 |
generative models | Why generative models produce mesh structure at the beginning? | https://ai.stackexchange.com/questions/46021/why-generative-models-produce-mesh-structure-at-the-beginning | <p>I don't really understand the reason of this. I have listed the outputs of different models below.</p>
<p>Gan (source: self made simple GAN for CIFAR10)</p>
<p><a href="https://i.sstatic.net/U0gJW0ED.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/U0gJW0ED.png" alt="enter image description here" /></a... | 465 | |
generative models | Are the newer OpenAI models such as o1 not generative pretrained transformers? | https://ai.stackexchange.com/questions/47974/are-the-newer-openai-models-such-as-o1-not-generative-pretrained-transformers | <p>OpenAI seems to be avoiding branding their reasoning models as "GPTs. See, for example, <a href="https://platform.openai.com/docs/models" rel="nofollow noreferrer">this page</a> from their API docs, which has one column for "GPT models" and another for "Reasoning models." Are the reasoning m... | <p>It could be said primarily as a functional branding decision since they're still foundationally based on pretrained LLM transformers for contextual language semantic understanding and generation, though they also differ significantly both in terms of fine-tuning methods and generated token types. Traditional GPT mod... | 466 |
generative models | Is image generation not existent before generative adversarial networks? | https://ai.stackexchange.com/questions/30055/is-image-generation-not-existent-before-generative-adversarial-networks | <p>Although the GAN is widely used due to its capability, there were generative models before the GAN which are based on probabilistic graphical models such as Bayesian networks, Markov networks, etc.</p>
<p>It is now a well-known fact that GANs are excelling at image generation tasks. But I am not sure whether the gen... | 467 | |
generative models | Given the generative model $P(A, B, C)$, would it be faster to learn $P(A,B,C,D)$, or compose $P(A, B, C)$ with $P(D \mid A,B,C)$? | https://ai.stackexchange.com/questions/12128/given-the-generative-model-pa-b-c-would-it-be-faster-to-learn-pa-b-c-d | <p>If we have a neural network that learns the generative model for <span class="math-container">$P(A, B, C)$</span>, the joint PDF of the random variables <span class="math-container">$A$</span>, <span class="math-container">$B$</span>, and <span class="math-container">$C$</span>.</p>
<p>Now, we want to learn the gene... | 468 | |
generative models | Which generative model architecture (and loss function) should I use to train on a dataset of 3D arrays of embedded tokens? | https://ai.stackexchange.com/questions/46940/which-generative-model-architecture-and-loss-function-should-i-use-to-train-on | <p>I've curated a dataset of player-made Minecraft builds. Each unique Minecraft block is tokenized and treated as a unique "word" like in NLP. I've trained a Skip-Gram model on the dataset (using context "cubes" and target "blocks" as opposed to windows and words). Plotting the embeddings... | 469 | |
generative models | How exactly does adversarial training help in handling mode-collapse in generative networks? | https://ai.stackexchange.com/questions/16441/how-exactly-does-adversarial-training-help-in-handling-mode-collapse-in-generati | <p>Of my understanding mode-collapse is when there happen to be multiple classes in the dataset and the generative network converges to only one of these classes and generates images only within this class. On training the model more, the model converges to another class.</p>
<p>In Goodfellows NeurIPS presentation he ... | <p>I don't think he said that at all. Going back to the talk you'll see he mentions mode collapse comes from the naivete of using alternating gradient-based optimization steps because then <span class="math-container">$min_{\phi}max_{\theta}L(G_\phi, D_\theta)$</span> starts to look a lot like <span class="math-contain... | 470 |
generative models | What is the difference between the term "generative" in classical machine learning and deep learning? | https://ai.stackexchange.com/questions/38925/what-is-the-difference-between-the-term-generative-in-classical-machine-learni | <p>There are lots of explanations on DGM (Deep Generative Model) and generative classifier (most of the explanations on which are about generative classifier vs discriminative classifier)</p>
<p>But, I can hardly find the common parts between the two concepts. In my understanding, 'generative' from DGM is quite straigh... | <p>Generative models have in common that they all model the distribution of the training samples. This makes it possible to sample from a distribution that is (hopefully) similar to the training data distribution. Different architecture types such as energy-based models, variational autoencoders, generative adversarial... | 471 |
generative models | Why do hypercube latent spaces perform poorer than Gaussian latent spaces in generative neural networks? | https://ai.stackexchange.com/questions/21129/why-do-hypercube-latent-spaces-perform-poorer-than-gaussian-latent-spaces-in-gen | <p>I have a quick question regarding the use of different latent spaces to represent a distribution. Why is it that a Gaussian is usually used to represent the latent space of the generative model rather than say a hypercube? Is it because a Gaussian has most of its distribution centred around the origin rather than a... | 472 | |
generative models | Is there such generative model that drawing idealized version of dataset? | https://ai.stackexchange.com/questions/46962/is-there-such-generative-model-that-drawing-idealized-version-of-dataset | <p>Suppose there are dataset of handwritten digit of zero. We know that human handwritten are not perfects. But here the model will tries to draw the <strong>ideal version</strong> of the digit, e.g. perfect circle.</p>
<p>Because I accidentally trained GAN where all random latent vectors input is almost identical to t... | <p>Turns out if we feed the generator part without discriminator with real samples and random latent, we got idealized version of the dataset.</p>
<p><a href="https://i.sstatic.net/pSJ6BVfg.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pSJ6BVfg.png" alt="enter image description here" /></a></p>
| 473 |
generative models | What does it mean to "learn a distribution", and what does it contain? | https://ai.stackexchange.com/questions/45040/what-does-it-mean-to-learn-a-distribution-and-what-does-it-contain | <p>When I was reading about discriminative vs generative models, I came across their definitions:</p>
<blockquote>
<p>Given a distribution of inputs <span class="math-container">$X$</span> and labels <span class="math-container">$Y:$</span></p>
<p>Discriminative models learn the conditional distribution <span class="ma... | <p><strong>Learning the distribution</strong>: When we talk about learning a distribution, we are essentially trying to capture the underlying statistical properties of the data. In other words, we try to capture the distribution from which the data points in our dataset our sampled. This involves estimating parameters... | 474 |
generative models | Which probability distribution a generator in Generative Adversarial Network (GAN) is capturing: dataset or ground truth? | https://ai.stackexchange.com/questions/29946/which-probability-distribution-a-generator-in-generative-adversarial-network-ga | <p>Consider the following statement from the abstract of the paper titled <a href="https://arxiv.org/pdf/1406.2661.pdf" rel="nofollow noreferrer">Generative Adversarial Nets</a></p>
<blockquote>
<p>We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train t... | 475 | |
generative models | Pointers to (deep) latent variable models that admit analytical approximations | https://ai.stackexchange.com/questions/42418/pointers-to-deep-latent-variable-models-that-admit-analytical-approximations | <p>I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-linear mapping (typically neural network).</p>
<p>In more traditional methods such as probabilistic PCA, the latent va... | 476 | |
generative models | How can I improve this toy Graph Neural Network Generative Language model | https://ai.stackexchange.com/questions/41921/how-can-i-improve-this-toy-graph-neural-network-generative-language-model | <h3>Background</h3>
<p>I'm an undergraduate student with research interests in a field of physics that has significant overlap with graph theory, and a functioning knowledge of how simple neural nets work and how to build them with TensorFlow and Keras. As many people are, I'm fascinated by the recent advancements in t... | <p>First of all, I would like to encourage you to keep trying new things, it sounds super fun! There are a few things I would like you to clarify about Graph Neural Networks (GNNs) and Graph Convolutional Neural Networks (GCNs), and then I will answer your questions as best I can. Also, as a sidenote, there are too man... | 477 |
generative models | Why is the last layer of a DBN or DBM used for classification task? | https://ai.stackexchange.com/questions/9933/why-is-the-last-layer-of-a-dbn-or-dbm-used-for-classification-task | <p>I understand why deep generative models like DBN ( deep belief nets ) or DBM ( deep boltzmann machines ) are able to capture underlying structures in data and use it for various tasks ( classification, regression, multimodal representations etc ...).</p>
<p>But for the classification tasks like in <a href="http://... | <p>One of the big realizations that deep learning models brought in recent years was that we can train the feature extractors and classifiers simultaneously. In fact most people have stopped separating the 2 tasks and simply refer to all the process as training the model. </p>
<p>However, if you dive in to every singl... | 478 |
generative models | What are the leading methods to estimate Epistemic Uncertainty in Large Language Models? | https://ai.stackexchange.com/questions/48038/what-are-the-leading-methods-to-estimate-epistemic-uncertainty-in-large-language | <p>Epistemic uncertainty is uncertainty that arises from a lack of knowledge, for instance in machine learning epistemic uncertainty can be caused by a lack of training data. Estimating epistemic uncertainty is important for useful AI systems, since it allows the AI to "know that it doesn't know", therefore a... | <p>In traditional Monte Carlo dropout approach, by enabling dropout at <em>inference</em> time and performing multiple stochastic forward passes, you can sample different outputs from the same input. The variability across these samples can serve as a proxy for epistemic uncertainty.</p>
<p>In LLMs recent work on chain... | 479 |
generative models | Should I need to interpret the word "metric" in "performance metric" rigorously? | https://ai.stackexchange.com/questions/35100/should-i-need-to-interpret-the-word-metric-in-performance-metric-rigorously | <p>Consider the following abstract from the research paper titled <a href="https://arxiv.org/pdf/1801.01973.pdf" rel="nofollow noreferrer">A Note on the Inception Score</a> for instance</p>
<blockquote>
<p>Deep generative models are powerful tools that have produced
impressive results in recent years. These advances ha... | <p>"Metric" should be understood as "a function of the trained model and of a dataset which returns a number".</p>
<p>For example, in reinforcement learning, one can use as an evaluation metric the total cumulative discounted reward that a policy gets on a set of episodes for a given task, starting ... | 480 |
generative models | Are there any Generative Adversarial Networks without Multi Layer Perceptrons? | https://ai.stackexchange.com/questions/29947/are-there-any-generative-adversarial-networks-without-multi-layer-perceptrons | <p>Although the main stream research is on Generative Adversarial Networks(GANs) using Multi Layer Percepteons (MLPs). The original paper titled <a href="https://arxiv.org/pdf/1406.2661.pdf" rel="nofollow noreferrer">Generative Adversarial Nets</a> clealry says, in abstract, that GAN is possible with out MLP also</p>
<... | 481 | |
generative models | Why is domain adaptation and generative modelling for knowledge graphs still not applied widely in enterprise data? What are the challenges? | https://ai.stackexchange.com/questions/23396/why-is-domain-adaptation-and-generative-modelling-for-knowledge-graphs-still-not | <p>I see that domain adaptation and transfer learning has been widely adopted in image classification and semantic segmentation analysis. But it's still lacking in providing solutions to enterprise data, for example, solving problems related to business processes?</p>
<p>I want to know what characteristics of the data ... | 482 | |
generative models | Do full-text translators such as DeepL or Google Translate fall under the term "Generative AI"? | https://ai.stackexchange.com/questions/43554/do-full-text-translators-such-as-deepl-or-google-translate-fall-under-the-term | <p>My question relates to full-text translators that are not specifically based on LLMs. My current understanding is that the term Generative AI goes beyond LLMs and that the full-text translators (especially those which are based on artificial neural networks) also fall into this category.</p>
<p><a href="https://en.w... | <p>Generative AI, as defined by IBM research, refers to deep-learning models capable of creating new content, be it text, images, or other media, based on their training data. This definition indeed encompasses models like GPT-3 or GPT-4, which can generate text in various styles and formats, including translations.</p... | 483 |
generative models | What are the differences between CRF and HMM? | https://ai.stackexchange.com/questions/13691/what-are-the-differences-between-crf-and-hmm | <p>What I know about CRF is that they are discriminative models, while HMM are generative models, but, in the inference method, both use the same algorithm, that is, the Viterbi algorithm, and forward and backward algorithms.</p>
<p>Does CRF use the same features as HMM, namely features transition and state features? ... | 484 | |
generative models | Is there a way to inject linear constrains during GAN training? | https://ai.stackexchange.com/questions/34762/is-there-a-way-to-inject-linear-constrains-during-gan-training | <p>Given that I'm training a generative model, (say a <em>generative adversarial network</em>), and I know that my (real) inputs (let's say vectors <span class="math-container">$\textbf{x} \in \mathbb{R}^n$</span>) satisfy linear constraints of the form e.g. <span class="math-container">$a_1\textbf{x}_1 + \dots a_n\tex... | <p>Maybe a bit too trivial to work out of the shelf, but I would try to add a component to the adversarial loss based precisely on the given set of coefficients.</p>
<p>Something like:</p>
<p><span class="math-container">$L_{linear}= \frac{\gamma}{k} \prod_{k}A_{k}\hat{x}$</span></p>
<p>which combined with the adversar... | 485 |
generative models | How does the VAE learn a joint distribution? | https://ai.stackexchange.com/questions/32161/how-does-the-vae-learn-a-joint-distribution | <p>I found the following paragraph from <a href="https://arxiv.org/abs/1906.02691" rel="nofollow noreferrer">An Introduction to
Variational Autoencoders</a> sounds relevant, but I am not fully understanding it.</p>
<blockquote>
<p>A VAE learns stochastic mappings between an observed <span class="math-container">$\mathb... | <p>The VAE models the following directed graphical model (figure 1 from the original VAE paper)</p>
<p><a href="https://i.sstatic.net/BLCT7.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BLCT7.png" alt="enter image description here" /></a></p>
<p>So, you have 2 sets of parameters, <span class="math-cont... | 486 |
generative models | Confusion between function learned and the underlying distribution | https://ai.stackexchange.com/questions/25557/confusion-between-function-learned-and-the-underlying-distribution | <p>Let us assume that I am working on a dataset of black and white dog images.</p>
<p>Each image is of size <span class="math-container">$28 \times 28$</span>.</p>
<p>Now, I can say that I have a sample space <span class="math-container">$S$</span> of all possible images. And <span class="math-container">$p_{data}$</sp... | <p>You're right! The generative model <span class="math-container">$f$</span> is not the same as the probability density (p.d.f.) function <span class="math-container">$p_{data}$</span>. The kind of phrases you've referred to are to be interpreted informally. You learn <span class="math-container">$f$</span> with the ... | 487 |
generative models | Is there any difference between 'input' and 'conditional input' in the case of neural networks? | https://ai.stackexchange.com/questions/30025/is-there-any-difference-between-input-and-conditional-input-in-the-case-of-n | <p>In the research paper titled <a href="https://arxiv.org/pdf/1411.1784.pdf" rel="nofollow noreferrer">Conditional Generative Adversarial Nets</a> by <em>Mehdi Mirza and Simon Osindero</em>, there is a notion of conditioning a neural network on a class label.</p>
<p>It is mentioned in the abstract that we need to <str... | 488 | |
generative models | Is there a continuous conditional variational auto-encoder? | https://ai.stackexchange.com/questions/13698/is-there-a-continuous-conditional-variational-auto-encoder | <p>The <a href="https://wiseodd.github.io/techblog/2016/12/17/conditional-vae/" rel="nofollow noreferrer">Conditional Variational Autoencoder (CVAE)</a>, introduced in the paper <a href="https://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models.pdf" rel="nofoll... | <p>Whether a discrete or continuous class, you can model it the same.</p>
<p>Denote the encoder <span class="math-container">$q$</span> and the decoder <span class="math-container">$p$</span>. Recall the variational autoencoder's goal is to minimize the <span class="math-container">$KL$</span> divergence between <span ... | 489 |
generative models | how can a VAE learn to generate a style for Neural Style Transfer? | https://ai.stackexchange.com/questions/34515/how-can-a-vae-learn-to-generate-a-style-for-neural-style-transfer | <p>I have come across <a href="https://arxiv.org/pdf/2110.07375.pdf" rel="nofollow noreferrer">this</a> research paper where a Variational Autoencoder is used to map multiple styles from reference images to a linear latent space and then transfer the style to another image like this:
<a href="https://i.sstatic.net/Z77h... | 490 | |
generative models | Why is ancestral sampling used in autoregressive models? | https://ai.stackexchange.com/questions/28384/why-is-ancestral-sampling-used-in-autoregressive-models | <p>I have been reading about <a href="https://deepai.org/machine-learning-glossary-and-terms/autoregressive-model" rel="nofollow noreferrer">autoregressive models</a>. Based on what I've read, it seems to me that all autoregressive models use <a href="https://www.reddit.com/r/deeplearning/comments/cgqpde/what_is_ancest... | <p>My understanding is that the answer to this question is basically 'ancestral sampling is used in autoregressive models because it fits well the structure/dynamics of autoregressive models (the ancestor-descendent relationship, etc.)'. It's not a very satisfying answer, but my understanding is that it's correct.</p>
... | 491 |
generative models | What should be taken as random variables in the distributions of datasets? | https://ai.stackexchange.com/questions/36296/what-should-be-taken-as-random-variables-in-the-distributions-of-datasets | <p>Consider the following two paragraphs taken from the paper titles <strong><a href="https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf" rel="nofollow noreferrer">Generative Adversarial Nets</a></strong> by <em>Ian J. Goodfellow et.al</em></p>
<p>#1: <strong>Abstract</strong></p>... | <p>The main reason of using the term <em>data distribution</em> over the <em>random variable</em> is to note for the intrinsic relationship the different data samples have with each other.</p>
<blockquote>
<p>It is a mathematical way of saying this data is almost random
(consider the average pixel on a <em>human face d... | 492 |
generative models | How are certain machine learning models able to produce variable-length outputs given variable-length inputs? | https://ai.stackexchange.com/questions/26531/how-are-certain-machine-learning-models-able-to-produce-variable-length-outputs | <p>Most machine learning models, such as multilayer perceptrons, require a fixed-length input and output, but generative (pre-trained) transformers can produce sentences or full articles of variable length. How is this possible?</p>
| <p>In short, repetition with feedback.</p>
<p>You are correct that machine learning (ML) models such as neural networks work with fixed dimensions for input and output. There are a few different ways to work around this when desired input and output are more variable. The most common approaches are:</p>
<ul>
<li><p><st... | 493 |
generative models | Connexion between noise and score in Diffusion models | https://ai.stackexchange.com/questions/47142/connexion-between-noise-and-score-in-diffusion-models | <p>I'm new to diffusion models so I'm trying to familiarize myself with the theory.</p>
<p>In the article <a href="https://arxiv.org/abs/2011.13456" rel="nofollow noreferrer">Score-Based Generative Modeling through Stochastic Differential Equations</a> (Song and al.), it's explained that we need to solve the reverse-ti... | <p>The loss function for noise prediction in DDPMs is typically the mean squared error between the true noise and the predicted noise formally expressed as <span class="math-container">$\mathbb{E}_{\mathbf{x}_0,ϵ,t}[∥ϵ−\hat{ϵ}_θ(\mathbf{x}_t,t)∥^2]$</span> which turns out to be equivalent to score matching objective <e... | 494 |
generative models | Could generative AI bootstrap? | https://ai.stackexchange.com/questions/41039/could-generative-ai-bootstrap | <p>In programming, if a new language could be improved by the language itself, it’s call self-hosting or bootstrapping.</p>
<p>To develop generative AI, there’s some steps, data preparing, model training, fine tuning. Is it possible to use AI it self to help with these steps and make big improvement in efficiency?</p>
| <p>Let me see if this helps-</p>
<p>I believe what you request is an understanding of whether or not Language models generate content to train themselves or other SOTA models.</p>
<p>In short yes, Language models can automate tasks like data pre-processing, and data-preparing, and given the right algorithm (consider Op... | 495 |
generative models | Do We Still Need to Learn About Boltzmann Machines? | https://ai.stackexchange.com/questions/46048/do-we-still-need-to-learn-about-boltzmann-machines | <p>When looking at the deep learning courses offered by top universities in the United States that are available online (not MOOCs, but actual classes), a few schools still cover (Restricted) Boltzmann Machines, although they are not many. However, I noticed that these techniques are not frequently applied nowadays. It... | 496 | |
generative models | Are there free and open-source audio versions of Generative AI programs like Stable Diffusion? | https://ai.stackexchange.com/questions/37734/are-there-free-and-open-source-audio-versions-of-generative-ai-programs-like-sta | <p>Generative AI is being used to create amazing art; first through paid services like Midjourney and now also with free, open source alternatives like Stable Diffusion. Now you can even generate art in a particular style, first with Google's Dreambooth and later with open-source implementations of the same.</p>
<p>Is ... | <p>You can check <a href="https://github.com/Harmonai-org/sample-generator" rel="nofollow noreferrer">sample-generator</a>, an implementation of stable diffusion for audio data from <a href="https://www.harmonai.org/" rel="nofollow noreferrer">Harmonai</a>. I'm playing with it recently and I can say it works pretty wel... | 497 |
generative models | How can one intuitively understand generative v/s discriminative models, specifically with respect to when each is useful? | https://ai.stackexchange.com/questions/2106/how-can-one-intuitively-understand-generative-v-s-discriminative-models-specifi | <p>I'm trying to gain some intuition beyond definitions, in any possible dimension. I'd appreciate references to read.</p>
| <p>The intution that I have about these is that generative are "from abstract to concrete" whereas discriminative models are "from concrete to abstract".</p>
<p>For example: Detecting if a photo has a cat or not is about going from the photo i.e concrete to the abstract concept of a cat. Whereas generating a photo of ... | 498 |
generative models | Comparing Auto-regressive Encoder-Decoders and Topological Neural Networks | https://ai.stackexchange.com/questions/41030/comparing-auto-regressive-encoder-decoders-and-topological-neural-networks | <p>I am interested in what insights can be gained about the mathematical class of auto-regressive encoder-decoders (LLMs), by comparing them to topological neural networks.</p>
<p>Specifically, I am looking for similarities and differences in their structures, behaviors, and mathematical properties.</p>
<p>In the conte... | 499 |
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