ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

⚔️ Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

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.

Robust Reinforcement Learning with the Alternating Training of Learned Adversaries (ATLA) framework

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".

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection

Repository for the paper "An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs"

Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification

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.

A collection of algorithms for detecting and handling label noise

FLPoison: Benchmarking Poisoning Attacks and Defenses in Federated Learning

Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix

The official implementation code of Paper "PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification" in AAAI 2024 (Oral)

"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)

👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)

MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers

Investigation of the effects of adversarial attacks and adversarial training on different variants of LSTM and CNN.