ashafaei / OD-testLinks
OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)
☆62Updated last year
Alternatives and similar repositories for OD-test
Users that are interested in OD-test are comparing it to the libraries listed below
Sorting:
- Release of CIFAR-10.1, a new test set for CIFAR-10.☆223Updated 5 years ago
- PyTorch Implementations of Dropout Variants☆87Updated 7 years ago
- ☆66Updated 6 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- Principled Detection of Out-of-Distribution Examples in Neural Networks☆202Updated 8 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆31Updated 5 years ago
- Gold Loss Correction☆87Updated 6 years ago
- SmoothGrad implementation in PyTorch☆172Updated 4 years ago
- Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)☆96Updated 2 years ago
- Robust Out-of-distribution Detection in Neural Networks☆73Updated 3 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆267Updated 4 years ago
- Pytorch Implementation of recent visual attribution methods for model interpretability☆146Updated 5 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆60Updated 4 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆176Updated 7 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Robust loss functions for deep neural networks (CVPR 2017)☆91Updated 5 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆128Updated last year
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Updated last year
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- Outlier Exposure with Confidence Control for Out-of-Distribution Detection☆71Updated 4 years ago
- PyTorch Implementation of Neural Statistician☆60Updated 3 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆35Updated 5 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 4 years ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆128Updated 5 years ago
- Code for Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks☆31Updated 7 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆162Updated 5 years ago