url stringlengths 36 82 | name stringlengths 2 143 | full_name stringlengths 2 143 ⌀ | description stringlengths 0 9.95k | paper dict | introduced_year int64 1.95k 2.02k | source_url stringlengths 32 228 ⌀ | source_title stringlengths 9 170 ⌀ | code_snippet_url stringclasses 464
values | num_papers int64 0 37.4k | collections listlengths 0 6 |
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{
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
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{
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} | 2,000 | http://arxiv.org/abs/1610.02915v4 | Deep Pyramidal Residual Networks | https://github.com/dyhan0920/PyramidNet-PyTorch/blob/5a0b32f43d79024a0d9cd2d1851f07e6355daea2/PyramidNet.py#L14 | 8 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Model Blocks"
},
{
"area": "General",
"area_id": "general",
"collection": "Skip Connection Blocks"
}
] |
https://paperswithcode.com/method/self-adaptive-training | Self-adaptive Training | Self-adaptive Training | **Self-adaptive Training** is a training algorithm that dynamically corrects problematic training labels by model predictions to improve generalization of deep learning for potentially corrupted training data. Accumulated predictions are used to augment the training dynamics. The use of an exponential-moving-average sc... | {
"title": "Self-Adaptive Training: beyond Empirical Risk Minimization",
"url": "https://paperswithcode.com/paper/self-adaptive-training-beyond-empirical-risk"
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{
"area": "General",
"area_id": "general",
"collection": "Robust Training"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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"area": "General",
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{
"area": "General",
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] | |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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[instance norma... | {
"title": "A Learned Representation For Artistic Style",
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{
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}
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
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}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Face Mesh Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
"url": "https://paperswithcode.com/paper/0-1-deep-neural-networks-via-block-coordinate"
} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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"title": "Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules",
"url": "https://paperswithcode.com/paper/inception-yolo-computational-cost-and"
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Object Detection Models"
}
] | |
https://paperswithcode.com/method/aspp | ASPP | Atrous Spatial Pyramid Pooling | **Atrous Spatial Pyramid Pooling (ASPP)** is a semantic segmentation module for resampling a given feature layer at multiple rates prior to [convolution](https://paperswithcode.com/method/convolution). This amounts to probing the original image with multiple filters that have complementary effective fields of view, thu... | {
"title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs",
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} | 2,000 | http://arxiv.org/abs/1606.00915v2 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs | 83 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Semantic Segmentation Modules"
}
] | |
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"title": "EfficientPose: Scalable single-person pose estimation",
"url": "https://paperswithcode.com/paper/efficientpose-scalable-single-person-pose"
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{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "Image Representations"
}
] | ||
https://paperswithcode.com/method/transferqa | TransferQA | TransferQA | **TransferQA** is a transferable generative QA model, built upon [T5](https://paperswithcode.com/method/t5) that combines extractive QA and multi-choice QA via a text-to-text [transformer](https://paperswithcode.com/method/transformer) framework, and tracks both categorical slots and non-categorical slots in DST. In ad... | {
"title": "Zero-Shot Dialogue State Tracking via Cross-Task Transfer",
"url": "https://paperswithcode.com/paper/zero-shot-dialogue-state-tracking-via-cross"
} | 2,000 | https://arxiv.org/abs/2109.04655v1 | Zero-Shot Dialogue State Tracking via Cross-Task Transfer | null | 1 | [
{
"area": "Natural Language Processing",
"area_id": "natural-language-processing",
"collection": "Question Answering Models"
}
] |
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"title": "0/1 Deep Neural Networks via Block Coordinate Descent",
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} | 2,000 | https://arxiv.org/abs/2206.09379v2 | 0/1 Deep Neural Networks via Block Coordinate Descent | null | 1 | [
{
"area": "Computer Vision",
"area_id": "computer-vision",
"collection": "3D Object Detection Models"
}
] |
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{
"area": "General",
"area_id": "general",
"collection": "2D Parallel Distributed Methods"
}
] |
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{
"area": "General",
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"title": "0-1 phase transitions in sparse spiked matrix estimation",
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{
"area": "Computer Vision",
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"collection": "3D Representations"
}
] |
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{
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{
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{
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{
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{
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{
"area": "Computer Vision",
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