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..
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Authors: Andy Zhou, Jindong Wang, Yu-Xiong Wang, Haohan Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results |
| Researcher Affiliation | Collaboration | 1University of Illinois at Urbana-Champaign 2Microsoft Research 3AI@UIUC EMAIL, EMAIL |
| Pseudocode | No | The paper presents mathematical equations and describes procedures in prose, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1code at https://github.com/andyz245/Discrete Adversarial Distillation |
| Open Datasets | Yes | Datasets. We train our models on Image Net-1K [13]. |
| Dataset Splits | No | The paper mentions training on ImageNet-1K and evaluating on various ImageNet variants (ImageNet-V2, ImageNet A, ImageNet-Sketch, ImageNet-Rendition, ImageNet-C, Stylized-ImageNet), but it does not specify explicit train/validation/test splits with percentages, sample counts, or explicit references to standard validation splits. |
| Hardware Specification | Yes | We conduct all of our experiments on 8 32GB NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using VQGAN, Aug Reg configurations, and references to hyperparameter settings from other papers, but does not specify software versions (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | For knowledge distillation, we use a temperature of t = 4 for all models and a = 0.5, following [63]. For DAD, we also weight the second KL-divergence term by a. All Vi T models are trained with the Aug Reg [59] hyperparameter and data augmentation configurations. We use one iteration for the adversarial attack, and an attack learning rate of 0.1. |