robustness
Model robustness testing
Evaluates and benchmarks the robustness of deep learning models to various corruptions and perturbations in computer vision tasks.
Corruption and Perturbation Robustness (ICLR 2019)
1k stars
14 watching
145 forks
Language: Python
last commit: about 2 years ago computer-visionconvolutional-neural-networksdeep-learningdomain-generalizationimagenetmachine-learningml-safetypytorchrobustness
Related projects:
| Repository | Description | Stars |
|---|---|---|
 guanghelee/neurips19-certificates-of-robustness | Tight certificates of adversarial robustness for randomly smoothed classifiers | 17 |
 borealisai/advertorch | A toolbox for researching and evaluating robustness against attacks on machine learning models | 1,308 |
 google-research/robustness_metrics | A toolset to evaluate the robustness of machine learning models | 466 |
 robustbench/robustbench | A standardized benchmark for measuring the robustness of machine learning models against adversarial attacks | 667 |
 edisonleeeee/greatx | A toolbox for graph reliability and robustness against noise, distribution shifts, and attacks. | 83 |
 max-andr/provably-robust-boosting | Provides provably robust machine learning models against adversarial attacks | 50 |
 illidanlab/fedrbn | An implementation of Federated Robustness Propagation in PyTorch to share robustness across heterogeneous federated learning users. | 26 |
 eth-sri/diffai | Trains neural networks to be provably robust against adversarial examples using abstract interpretation techniques. | 218 |
 sail-sg/mmcbench | A benchmarking framework designed to evaluate the robustness of large multimodal models against common corruption scenarios | 27 |
 ucsc-vlaa/vllm-safety-benchmark | A benchmark for evaluating the safety and robustness of vision language models against adversarial attacks. | 67 |
 aka-discover/ccmba_cvpr23 | Improving semantic segmentation robustness to motion blur using custom data augmentation techniques | 5 |
 kaiyangzhou/dassl.pytorch | A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,217 |
 cchio/deep-pwning | A tool to test the vulnerability of machine learning models to adversarial attacks | 559 |
 madrylab/robustness | A library for training and evaluating neural networks with a focus on adversarial robustness. | 918 |
 kaist-dmlab/selfie | A method to enhance robustness in deep learning by selectively refining noisy training data and combining it with clean samples. | 50 |