Jeckmu/Erase-at-the-Core

Erase at the Core checkpoints

Official checkpoints for Erase at the Core: Representation Unlearning for Machine Unlearning. This model repository provides the original backbones, retrained oracles, pretrained Erase-at-the-Core (EC) modules, and final EC-unlearned models needed to reproduce the released ImageNet-1K and CIFAR-100 experiments.

• Paper: arXiv:2602.05375
• Code: 220v-K/EC_Unlearning
• Checkpoints: Jeckmu/Erase-at-the-Core

The code repository contains the launchers, environment file, and complete reproduction guide for these checkpoints.

Available checkpoints

Dataset	Backbone	Forget benchmark	Original	Retrained	EC modules	Final EC model
ImageNet-1K	ResNet-50	Random-100	imagenet1k/resnet50/original.pth.tar	imagenet1k/resnet50/retrained_random100.pth.tar	imagenet1k/resnet50/ec_modules.tar	imagenet1k/resnet50/ec_unlearned_random100.tar
ImageNet-1K	ResNet-50	Top-100/CUB	imagenet1k/resnet50/original.pth.tar	imagenet1k/resnet50/retrained_top100_cub.pth.tar	imagenet1k/resnet50/ec_modules.tar	imagenet1k/resnet50/ec_unlearned_top100_cub.tar
CIFAR-100	ResNet-50	Random-10	cifar100/resnet50/original_cifar100.tar	cifar100/resnet50/retrained_cifar100_random10.tar	cifar100/resnet50/ec_modules_cifar100.tar	cifar100/resnet50/ec_unlearned_random10.tar
ImageNet-1K	Swin-Tiny	Random-100	imagenet1k/swin/original_swin.tar	imagenet1k/swin/retrained_swin_random100.tar	imagenet1k/swin/ec_modules_swin.tar	imagenet1k/swin/ec_unlearned_swin_random100.tar

The bundle contains 14 checkpoint files (approximately 2.0 GB). Shared original and EC-module checkpoints appear once in the repository even when they are used by multiple forget benchmarks.

Download

Install and authenticate the Hugging Face CLI when required:

python -m pip install -U huggingface_hub
hf auth login  # only for private or gated access

Download the complete release while preserving its directory structure:

export HF_REPO_ID="Jeckmu/Erase-at-the-Core"
hf download "$HF_REPO_ID" --local-dir ./checkpoints

Download only the files needed to evaluate ImageNet ResNet Random-100:

hf download "$HF_REPO_ID" \
  imagenet1k/resnet50/original.pth.tar \
  imagenet1k/resnet50/retrained_random100.pth.tar \
  imagenet1k/resnet50/ec_unlearned_random100.tar \
  --local-dir ./checkpoints

Use --revision <commit-or-tag> for a version-pinned download and --dry-run to inspect the transfer before downloading.

Use with the released code

Copy the code repository's .env.example to .env, set CHECKPOINT_ROOT=./checkpoints, and source it:

cp .env.example .env
set -a
source .env
set +a

The public file hierarchy already matches every checkpoint path in .env.example.

Start at EC-module pretraining

Download the corresponding original backbone, then run one of:

bash scripts/pretrain.sh ec-resnet
bash scripts/pretrain.sh ec-cifar
bash scripts/pretrain.sh ec-swin

Start at EC unlearning

Download the corresponding ec_modules*.tar file, then run one of:

bash scripts/run_unlearning.sh imagenet-random100-resnet50
bash scripts/run_unlearning.sh imagenet-top100-resnet50
bash scripts/run_unlearning.sh cifar100-random10-resnet50
bash scripts/run_unlearning.sh imagenet-random100-swin-tiny

Start directly at evaluation

Download the benchmark's original, retrained, and final EC-unlearned checkpoints. For example:

bash scripts/evaluate.sh table \
  imagenet-random100-resnet50 \
  "$RANDOM100_EC_UNLEARNED_CKPT"

The code-repository guide documents the table, idi, layerwise-cka, and tsne evaluation modes.

Checkpoint format

These files are PyTorch checkpoint dictionaries. Every file contains a state_dict; some also contain the epoch, optimizer/scheduler state, best metric, or stored evaluation results. The .tar suffix is a historical checkpoint filename and does not mean that the file should be extracted.

import torch

payload = torch.load(
    "checkpoints/imagenet1k/resnet50/ec_modules.tar",
    map_location="cpu",
)
state_dict = payload["state_dict"]
state_dict = {
    key.removeprefix("module."): value
    for key, value in state_dict.items()
}

Use the released project loaders for training or evaluation; they instantiate the appropriate plain or aux-head architecture and handle DataParallel prefixes. The checkpoints are not packaged for transformers.AutoModel or the hosted Inference API.

Important Swin configuration

The paper-default Swin experiment uses Swin_contrastive_auxheadfc.py with no layer-wise cross-entropy on intermediate EC heads:

cu_head_weights = 0.2,0.4,0.8,1.0
ce_head_weights = 0,0,0,1

The final classifier still receives retain-set cross-entropy.

Integrity

SHA256SUMS contains hashes for all 14 model files, and checkpoint_manifest.json records their roles, byte sizes, and checkpoint metadata. Verify a downloaded bundle with:

cd checkpoints
sha256sum -c SHA256SUMS

Intended use and limitations

These artifacts are intended for research reproduction and analysis of class-wise machine unlearning. Datasets are not included and must be obtained under their respective terms. The reported unlearning behavior is empirical; the checkpoints do not by themselves provide a formal privacy or deletion guarantee.

Citation

@article{lee2026erase,
  title   = {Erase at the Core: Representation Unlearning for Machine Unlearning},
  author  = {Lee, Jaewon and Kim, Yongwoo and Kim, Donghyun},
  journal = {arXiv preprint arXiv:2602.05375},
  year    = {2026},
  doi     = {10.48550/arXiv.2602.05375},
  url     = {https://arxiv.org/abs/2602.05375}
}

License

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