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..
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
Authors: Shiji Zhao, Ranjie Duan, xizhewang , Xingxing Wei
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that ABSLD outperforms state-of-theart AT, ARD, and robust fairness methods in the comprehensive metric (Normalized Standard Deviation) of robustness and fairness. |
| Researcher Affiliation | Collaboration | Shiji Zhao1, Ranjie Duan2, Xizhe Wang1, Xingxing Wei1 1Institute of Artificial Intelligence, Beihang University, Beijing, China 2Security Department, Alibaba Group, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1 Overview of ABSLD |
| Open Source Code | Yes | The code can be found in https://github.com/zhaoshiji123/ABSLD. |
| Open Datasets | Yes | We conduct our experiments on three datasets: CIFAR-10 [16], CIFAR-100, and Tiny-Image Net [17]. |
| Dataset Splits | No | The paper mentions using a validation strategy for checkpoint selection ("The checkpoint is selected based on the best checkpoint..."), but does not explicitly provide specific dataset split percentages or counts for validation. |
| Hardware Specification | Yes | All the experiments are conducted in a single Ge Force RTX 3090, and our ABSLD takes approximately one GPU day for training a model. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For ABSLD, we train the model using the Stochastic Gradient Descent (SGD) optimizer with an initial learning rate of 0.1, a momentum of 0.9, and a weight decay of 2e-4. The learning rate β of temperature is initially set as 0.1. For CIFAR-10 and CIFAR-100, we set the training epochs to 300. The learning rate is divided by 10 at the 215-th, 260-th, and 285-th epochs; We set the batch size to 128 for both CIFAR-10 and CIFAR-100 following [45]. For the inner maximization, we use a 10-step PGD with a random start size of 0.001 and a step size of 2/255, and the perturbation is bounded to the L norm ϵ = 8/255. |