Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously
Authors: Yihan Wang, Yifan Zhu, Xiao-Shan Gao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On experimental side, we evaluate the standard supervised learning algorithm and four representative contrastive learning algorithms, Sim CLR [5], Mo Co [7], BYOL [15] and Sim Siam [6]. Our proposed AUE and AAP attacks achieve the state-of-the-art worst-case unlearnability on CIFAR-10/100 and Tiny/Mini-Image Net datasets (see Section 5.2). |
| Researcher Affiliation | Academia | Yihan Wang, Yifan Zhu, Xiao-Shan Gao Academy of Mathematics and Systems Science, Chinese Academy of Sciences University of Chinese Academy of Sciences EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Augmented Unlearnable Examples (AUE) and Algorithm 2 Augmented Adversarial Poisoning (AAP) |
| Open Source Code | Yes | The code is available at https://github. com/Ehan W/AUE-AAP. |
| Open Datasets | Yes | We conduct experiments on CIFAR-10/100 [26], Tiny-Image Net [27], modified Mini-Image Net [49], and Image Net-100 [38]. |
| Dataset Splits | No | The paper describes training and test sets for CIFAR-10/100, Tiny-Image Net, and Mini-Image Net, but does not explicitly provide details for a separate validation dataset split. |
| Hardware Specification | Yes | For CIFAR-10/100, Tiny/Mini Image Net, experiments are conducted using a single NVIDIA Ge Force RTX 3090 GPU. For Image Net-100, experiments are conducted using a single NVIDIA A800 GPU. |
| Software Dependencies | No | We leverage differentiable augmentation modules in Kornia2 [37] which is a differentiable computer vision library for Py Torch. (No version numbers provided for Kornia or PyTorch). |
| Experiment Setup | Yes | AUE. We train the reference model for T = 60 epochs with SGD optimizer and cosine annealing learning rate scheduler. The batch size of training data is 128. The initial learning rate αθ is 0.1, weight decay is 10-4 and momentum is 0.9. In each epoch, we update the model for Tθ = 391 iterations and update poisons for Tδ = 391 iterations. ... The PGD process for noise generation takes Tp = 5 steps with step size αδ = 0.8/255. The augmentation strength s = 0.6 for CIFAR-10 and s = 1.0 for CIFAR-100, Tiny-Image Net, Mini Image Net, and Image Net-100. |