A curated (most recent) list of resources for Learning with Noisy Labels
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Updated
Feb 29, 2024
A curated (most recent) list of resources for Learning with Noisy Labels
Curated list of open source tooling for data-centric AI on unstructured data.
ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction
The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Robust Reinforcement Learning with the Alternating Training of Learned Adversaries (ATLA) framework
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.
pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation
A repository contains a collection of resources and papers on Imbalance Learning On Graphs
This is the code for our paper `Robust Federated Learning with Attack-Adaptive Aggregation' accepted by FTL-IJCAI'21.
[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
Randomized Smoothing of All Shapes and Sizes (ICML 2020).
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Reading list for adversarial perspective and robustness in deep reinforcement learning.
Repository for the paper "An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs"
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization
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.
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
Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification
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