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 [1].
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. |