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 professionally 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).
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability (ICCV 2023)
[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)
[NeurIPS 2023] Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Codes and datasets for ICML21 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”
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
Potential energy ranking for domain generalization (DG)
The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)
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