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..
Robust Pre-Training by Adversarial Contrastive Learning
Authors: Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate the proposed Adversarial Contrastive Learning (ACL) and show it can consistently outperform existing methods. For example on the CIFAR-10 dataset, ACL outperforms the previous state-of-the-art unsupervised robust pre-training approach [1] by 2.99% on robust accuracy and 2.14% on standard accuracy. |
| Researcher Affiliation | Collaboration | Ziyu Jiang1, Tianlong Chen2, Ting Chen3, Zhangyang Wang2 1Texas A&M University, 2University of Texas at Austin, 3Google Research, Brain Team EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm of Dual Stream (DS) Pretraining |
| Open Source Code | Yes | Our codes and pre-trained models have been released at: https: //github.com/VITA-Group/Adversarial-Contrastive-Learning. |
| Open Datasets | Yes | We evaluate three datasets: CIFAR-10, CIFAR-10-C [31], CIFAR-100. |
| Dataset Splits | Yes | The fine-tuned models are selected based on the held-out validation RA. Figure 3: The robust accuracy in cross-validation dataset w.r.t. different epochs. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library names or solvers (e.g., PyTorch version, TensorFlow version, CUDA version). |
| Experiment Setup | Yes | For contrastive pre-training, we identically follow Sim CLR [2] for all the optimizer settings, augmentation and projection head structure. We choose 512 for batch size and train for 1000 epochs. [...] We use SGD with 0.9 momentum and batch size 128. By default, we fine-tune 25 epochs, with initial learning rate set as 0.1 and then decaying by 10 times at epoch 15 and 20. |