--- license: mit tags: - machine-unlearning - cifar10 - computer-vision - robustness - benchmarks ---

EasyDUB mascot

## EasyDUB Dataset ### Easy **D**ata **U**nlearning **B**ench Precomputed CIFAR-10 data for KLOM (KL-divergence of Margins) evaluation of data-unlearning methods. This dataset contains: - **200 pretrain models**: ResNet9 models trained on the full CIFAR-10 training set (50,000 samples). - **200 oracle models per forget set**: ResNet9 models retrained on the retain set (train minus forget) for each of 10 forget sets. - **Logits and margins**: Precomputed logits and margins for all models on train/val/forget/retain splits. All models are checkpointed at epoch 23 (out of 24 total training epochs). ### Directory structure The on-disk layout is: ```text EasyDUB-dataset/ ├── models/ │ └── cifar10/ │ ├── pretrain/ │ │ └── resnet9/ │ │ └── id_X_epoch_23.pt # 200 models (X = 0–199) │ └── oracle/ │ └── forget_Z/ │ └── resnet9/ │ └── id_X_epoch_23.pt # 200 models per forget set │ ├── logits/ │ └── cifar10/ │ ├── pretrain/ │ │ ├── retain/ │ │ │ └── resnet9/ │ │ │ └── id_X_epoch_23.npy # Full train set logits │ │ ├── val/ │ │ │ └── resnet9/ │ │ │ └── id_X_epoch_23.npy # Validation logits │ │ └── forget_Z/ │ │ └── resnet9/ │ │ └── id_X_epoch_23.npy # Forget-set logits │ └── oracle/ │ └── forget_Z/ │ ├── retain/ │ │ └── resnet9/ │ │ └── id_X_epoch_23.npy # Retain logits │ ├── forget/ │ │ └── resnet9/ │ │ └── id_X_epoch_23.npy # Forget logits │ └── val/ │ └── resnet9/ │ └── id_X_epoch_23.npy # Validation logits │ ├── margins/ │ └── cifar10/ │ └── [same structure as logits/] │ └── forget_sets/ └── cifar10/ └── forget_set_Z.npy # Indices into CIFAR-10 train set ``` ### File naming - **Models**: `id_{MODEL_ID}_epoch_{EPOCH}.pt` (e.g. `id_42_epoch_23.pt`) - **Logits / margins**: `id_{MODEL_ID}_epoch_{EPOCH}.npy` - **Forget sets**: `forget_set_{SET_ID}.npy` (e.g. `forget_set_1.npy`) ### Shapes and dtypes - **Logits**: `(n_samples, 10)` NumPy arrays of `float32` — raw model outputs for the 10 CIFAR-10 classes. - **Margins**: `(n_samples,)` NumPy arrays of `float32` — scalar margins (see formula below). - **Forget sets**: `(n_forget_samples,)` NumPy arrays of integer indices into the CIFAR-10 training set in `[0, 49_999]`. Typical sizes: - Train set: 50_000 samples - Validation set: 10_000 samples - Forget sets: 10–1000 samples (varies by set) ### Margin definition For each sample with logits `logits` and true label `true_label`: ```python import torch def compute_margin(logits: torch.Tensor, true_label: int) -> torch.Tensor: logit_other = logits.clone() logit_other[true_label] = -torch.inf return logits[true_label] - logit_other.logsumexp(dim=-1) ``` Higher margins indicate higher confidence in the correct class relative to all others (via log-sum-exp). ### Forget sets The dataset includes 10 CIFAR-10 forget sets: - **Forget set 1**: 10 random samples - **Forget set 2**: 100 random samples - **Forget set 3**: 1_000 random samples - **Forget set 4**: 10 samples with highest projection onto the 1st principal component - **Forget set 5**: 100 samples with highest projection onto the 1st principal component - **Forget set 6**: 250 samples with highest + 250 with lowest projection onto the 1st principal component - **Forget set 7**: 10 samples with highest projection onto the 2nd principal component - **Forget set 8**: 100 samples with highest projection onto the 2nd principal component - **Forget set 9**: 250 samples with highest + 250 with lowest projection onto the 2nd principal component - **Forget set 10**: 100 samples closest in CLIP image space to a reference cassowary image Each `forget_set_Z.npy` is a 1D array of training indices. ### Quick start The companion [EasyDUB-code](https://github.com/easydub/EasyDUB-code) repository provides utilities and unlearning methods on top of this dataset. Here is a minimal example using only NumPy and PyTorch: ```python import numpy as np import torch root = "EasyDUB-dataset" # Load margins for a single pretrain model on the validation set margins = np.load(f"{root}/margins/cifar10/pretrain/val/resnet9/id_0_epoch_23.npy") # Load oracle margins for the same model index and forget set (example: forget_set_1) oracle_margins = np.load( f"{root}/margins/cifar10/oracle/forget_1/val/resnet9/id_0_epoch_23.npy" ) print(margins.shape, oracle_margins.shape) ``` For a higher-level end-to-end demo (including unlearning methods and KLOM computation), see the [EasyDUB-code](https://github.com/easydub/EasyDUB-code) GitHub repository. In particular, `strong_test.py` in `EasyDUB-code` runs a reproducible noisy-SGD unlearning experiment comparing: - `KLOM(pretrain, oracle)` - `KLOM(noisy_descent, oracle)` ### Training procedure (summary) All pretrain and oracle models share the same training setup: - Optimizer: SGD with momentum - Learning rate: 0.4 (triangular schedule peaking at epoch 5) - Momentum: 0.9 - Weight decay: 5e-4 - Epochs: 24 total, checkpoint used here is epoch 23 - Mixed precision: enabled (FP16) - Label smoothing: 0.0 Pretrain models are trained on the full CIFAR-10 training set. Oracle models are trained on the retain set (training set minus the corresponding forget set) for each forget set. ### Citation If you use EasyDUB in your work, please cite: ```bibtex @misc{rinberg2026easydataunlearningbench, title={Easy Data Unlearning Bench}, author={Roy Rinberg and Pol Puigdemont and Martin Pawelczyk and Volkan Cevher}, year={2026}, eprint={2602.16400}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2602.16400}, } ``` EasyDUB builds on the KLOM metric introduced in: ```bibtex @misc{georgiev2024attributetodeletemachineunlearningdatamodel, title = {Attribute-to-Delete: Machine Unlearning via Datamodel Matching}, author = {Kristian Georgiev and Roy Rinberg and Sung Min Park and Shivam Garg and Andrew Ilyas and Aleksander Madry and Seth Neel}, year = {2024}, eprint = {2410.23232}, archivePrefix = {arXiv}, primaryClass = {cs.LG}, url = {https://arxiv.org/abs/2410.23232}, } ```