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..
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Authors: Hila Chefer, Idan Schwartz, Lior Wolf
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an extensive battery of experiments we show that (i) the classification accuracy on datasets from shifted domains increases considerably. This includes real-world unbiased and adversarial datasets, as well as synthetic ones that were created specifically to measure the robustness of the classification model, (ii) the resulting relevance maps demonstrate a significant improvement in focusing on the foreground of the image, i.e. the object, rather than on its background. |
| Researcher Affiliation | Academia | Hila Chefer Idan Schwartz Lior Wolf School of Computer Science Tel-Aviv University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/hila-chefer/Robust Vi T. |
| Open Datasets | Yes | We conduct our experiments on Image Net-v2 [39], Image Net-A [26], Image Net-R [25], Image Net-Sketch [56], Object Net [4], and SI-Score [13]. |
| Dataset Splits | Yes | We use 3 training images from 500 Image Net classes for our finetuning (overall1500 samples), and another 414 images as a validation set. |
| Hardware Specification | Yes | The small, base models are finetuned on a single RTX 2080 Ti GPU, and the large models on a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using "the implementation and pre-trained weights from [59]" which refers to "Pytorch image models" but does not specify version numbers for PyTorch or other libraries, which is required for reproducible ancillary software details. |
| Experiment Setup | Yes | All models are finetuned as described in Sec. 3 for 50 epochs, with a batch size of 8. We use 3 training images from 500 Image Net classes for our finetuning (overall1500 samples), and another 414 images as a validation set. The learning rate of each model is determined using a grid search between the values 5e 7 and 5e 6. All our experiments apply the same choice of λbg = 2, λfg = 0.3. The overall loss for the finetuning process is, therefore: L = λrelevance Lrelevance + λclassification Lclassification, where λrelevance = 0.8, and λclassification = 0.2 remain constant in all our experiments. |