Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification
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Updated
Dec 30, 2019 - Python
Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification
Repository for the paper "An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs"
Randomized Smoothing of All Shapes and Sizes (ICML 2020).
Robust Single-Linkage Clustering
Implementation of the paper: Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability (ICPR 2020)
Robust Reinforcement Learning with the Alternating Training of Learned Adversaries (ATLA) framework
Official implementation of the paper: "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions", IEEE WACV, 2022
Robust Object Detection Fusion Against Deception
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
Code of ICLR SRML paper titled "Fair Machine Learning under Limited Demographically Labeled Data"
A curated list of Robust Machine Learning papers/articles and recent advancements.
A curated list of Distribution Shift papers/articles and recent advancements.
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
Repository for the Reliable and Trustworthy AI course offered in Fall 2022 at ETH Zürich: implementation of DeepPoly, Robustness Analyzer for Deep Neural Networks
Repository for code release of preprint: "Repairing Systematic Outliers by Learning Clean Subspaces in VAEs".
Investigation of the effects of adversarial attacks and adversarial training on different variants of LSTM and CNN.
Applying K-Means and Agglomerative hierarchical clustering to dataset
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