Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
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Updated
May 10, 2024 - Python
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc
A curated list of trustworthy deep learning papers. Daily updating...
GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
Papers about out-of-distribution generalization on graphs.
[NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
[NeurIPS 2022] The official repository of Expression Learning with Identity Matching for Facial Expression Recognition
The Pytorch implementation for "Topology-aware Robust Optimization for Out-of-Distribution Generalization" (ICLR 2023)
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Code for ICML21 spotlight paper "Towards open-world recommendation: An inductive model-based collaborative filtering approach"
Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”
[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Potential energy ranking for domain generalization (DG)
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