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..
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Authors: Micah Goldblum, Liam Fowl, Tom Goldstein
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Model Anat Aadv Naturally Trained R2-D2 73.01 % 0.13 0.00 % 0.13 AT transfer (R2-D2 backbone) 39.13 % 0.13 25.33% 0.13 ADML 47.75% 0.13 18.49 % 0.13 AQ R2-D2 (ours) 57.87% 0.13 31.52% 0.13 Table 1: R2-D2 [3], adversarially trained transfer learning, ADML [33], and our adversarially queried (AQ) R2-D2 model on 5-shot Mini-Image Net. |
| Researcher Affiliation | Academia | Micah Goldblum Department of Mathematics University of Maryland EMAIL Liam Fowl Department of Mathematics University of Maryland EMAIL Tom Goldstein Department of Computer Science University of Maryland EMAIL |
| Pseudocode | Yes | Algorithm 1 The meta-learning framework |
| Open Source Code | Yes | A Py Torch implementation of adversarial querying can be found at: https://github.com/goldblum/Adversarial Querying |
| Open Datasets | Yes | We report performance on Omniglot, Mini-Image Net, and CIFAR-FS [16, 31, 3]. |
| Dataset Splits | No | The paper describes the use of support and query data for meta-learning but does not provide specific percentages or counts for the overall train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'A Py Torch implementation' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | A description of our training regime can be found in Appendix A.5. ... We consider perturbations with bound 8/255 and a step size of 2/255 as described by [19]. ... Layers are ο¬ne-tuned for 10 steps with a learning rate of 0.01. |