Collection of awesome test-time (domain/batch/instance) adaptation methods
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Updated
Aug 23, 2024
Collection of awesome test-time (domain/batch/instance) adaptation methods
The official code of IEEE S&P 2024 paper "Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability". We study how to train surrogates model for boosting transfer attack.
Frouros: an open-source Python library for drift detection in machine learning systems.
Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc
"RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning" by Yue Duan (ECCV 2022)
"Towards Semi-supervised Learning with Non-random Missing Labels" by Yue Duan (ICCV 2023)
This repository contains the code of the distribution shift framework presented in A Fine-Grained Analysis on Distribution Shift (Wiles et al., 2022).
A repository and benchmark for online test-time adaptation.
A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".
Official implementation of the Fréchet Radiomics Distance | pip install frd-score
Reinforcement Learning Environments for Sustainable Energy Systems
Code for "Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning"
The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.
NeurIPS22 "RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection"
Temporally and Distributionally Robust Optimization for Cold-start Recommendation (AAAI'24)
Lightweight, useful implementation of conformal prediction on real data.
GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
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