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---
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:**
**Algorithm Description:**
# 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
```
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