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 Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Authors: Xilie Xu, Jingfeng ZHANG, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the largescale Image Net-1K dataset to obtain an effective robust representation via RCS. |
| Researcher Affiliation | Academia | Xilie Xu1 , Jingfeng Zhang2,3 , Feng Liu4, Masashi Sugiyama2,5, Mohan Kankanhalli1 1 School of Computing, National University of Singapore 2 RIKEN Center for Advanced Intelligence Project (AIP) 3 School of Computer Science, The University of Auckland 4 School of Computing and Information Systems, The University of Melbourne 5 Graduate School of Frontier Sciences, The University of Tokyo |
| Pseudocode | Yes | Algorithm 1 Robustness-aware Coreset Selection (RCS) Algorithm 2 Efficient ACL via RCS |
| Open Source Code | Yes | Our source code is at https://github.com/GodXuxilie/Efficient_ACL_via_RCS. |
| Open Datasets | Yes | Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the largescale Image Net-1K dataset to obtain an effective robust representation via RCS. [1] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A largescale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248 255. Ieee, 2009. |
| Dataset Splits | Yes | Therefore, given an unlabeled training set X X N and an unlabeled validation set U X M (M N), our proposed Robustness-aware Coreset Selection (RCS) searches for a coreset S such that S = arg min S X,|S|/|X| k LRD(U; arg min θ LACL(S; θ)). |
| Hardware Specification | Yes | We conducted all experiments on Python 3.8.8 (Py Torch 1.13) with 4 NVIDIA RTX A5000 GPUs (CUDA 11.6). |
| Software Dependencies | Yes | We conducted all experiments on Python 3.8.8 (Py Torch 1.13) with 4 NVIDIA RTX A5000 GPUs (CUDA 11.6). |
| Experiment Setup | Yes | Efficient pre-training configurations. We leverage RCS to speed up ACL [14] and Dyn ACL [17] using Res Net-18 backbone networks. The pre-training settings of ACL and Dyn ACL exactly follow their original paper and we provide the details in Appendix B.2. For the hyperparameters of RCS, we set β = 512, η = 0.01, and TRCS = 3. We took W = 100 epochs for warmup, and then CS was executed every I = 20 epoch. We used different subset fractions k {0.05, 0.1, 0.2} for CS. |