README.md

---
license: mit
size_categories:
- 10K<n<100K
---
Dataset for the evaluation of data-unlearning techniques using KLOM (KL-divergence of Margins).

# How KLOM works:
KLOM works by:
1. training N models (original models)
2. Training N fully-retrained models (oracles) on forget set F
3. unlearning forget set F from the original models
4. Comparing the outputs of the unlearned models from the retrained models on different points
  (specifically, computing the KL divergence between the distribution of _margins_ of oracle models and distribution of _margins_ of the unlearned models)

Originally proposed in the work Attribute-to-Delete: Machine Unlearning via Datamodel Matching (https://arxiv.org/abs/2410.23232), described in detail in E.1.

**Outline of how KLOM works:**
![Image 5-4-25 at 9.21 PM.jpg](https://cdn-uploads.huggingface.co/production/uploads/6625510c9277b825c8c71418/RcbE1ucGOYgTnoRJmSKa4.jpeg)


**Algorithm Description:**
![Image 5-4-25 at 9.24 PM.jpg](https://cdn-uploads.huggingface.co/production/uploads/6625510c9277b825c8c71418/N3vJmc6rfQ5MLMjXSCIGZ.jpeg)

# Structure of Data

The overal structure is as follows:
```
full_models
  ├── CIFAR10
  ├── CIFAR10_augmented
  └── LIVING17
oracles
└── CIFAR10
    ├── forget_set_1
    ├── forget_set_2
    ├── forget_set_3
    ├── forget_set_4
    ├── forget_set_5
    ├── forget_set_6
    ├── forget_set_7
    ├── forget_set_8
    ├── forget_set_9
    └── forget_set_10
```
Each folder has 

* train_logits_##.pt - logits at the end of training for model `##` for validation points
* val_logits_##.pt  - logits at the end of training for model `##` for train points
* `##__val_margins_#.npy` - margins of model `##` at epoch `#` (this is derived from logits)
* `sd_##____epoch_#.pt` - model `##` checkpoint at epoch `#`


# How to download



Create script `download_folder.sh`

```
#!/bin/bash
REPO_URL=https://huggingface.co/datasets/royrin/KLOM-models
TARGET_DIR=KLOM-models # name it what you wish
FOLDER=$1  # e.g., "oracles/CIFAR10/forget_set_3"

mkdir -p $TARGET_DIR

git clone --filter=blob:none --no-checkout $REPO_URL $TARGET_DIR
cd $TARGET_DIR
git sparse-checkout init --cone
git sparse-checkout set $FOLDER
git checkout main
```
 
Example how to run script:
```
bash download_folder.sh oracles/CIFAR10/forget_set_3
```



## How forget sets generated

We have 10 different forget sets: sets 1,2,3 are random forget sets of sizes 10,100,1000 respectively; sets 4-9 correspond to semantically coherent subpopulations of examples (e.g., all dogs facing a similar direction) identified using clustering methods. 
Specifically, we take a $n \times n$ datamodel matrix constructed by concatenating ``train x train`` datamodels ($n=50,000$). Next, we compute the top principal components (PCs) of the influence matrix and construct the following forget sets:
* Forget set 1: 10 random samples
* Forget set 2: 100 random samples
* Forget set 3: 500 random samples
* Forget set 4: 10 samples with the highest projection onto the 1st PC
* Forget set 5: 100 samples with the highest projection onto the 1st PC
* Forget set 6: 250 samples with the highest projection onto the 1st PC and 250 with lowest projection
* Forget set 7: 10 samples with the highest projection onto the 2nd PC
* Forget set 8: 100 samples with the highest projection onto the 2nd PC
* Forget set 9: 250 samples with the highest projection onto the 2nd PC and 250 with the lowest projection.
* Forget set 10: 100 samples closest in CLIP image space to training example 6 (a cassowary)

\paragraph{ImageNet Living-17.} We use three different forget sets: 
* Forget set 1 is random of size 500;
* Forget sets 2 and 3 correspond to 200 examples from a certain subpopulation (corresponding to a single original ImageNet class) within the Living-17 superclass.