GradCAM-based Copy and Paste Augmentation
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Updated
Oct 10, 2022 - Python
GradCAM-based Copy and Paste Augmentation
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.
[NeurIPS 2023] “SODA: Robust Training of Test-Time Data Adaptors”
[NeurIPS 2023] Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
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
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
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)
The Pytorch implementation for "Are Data-driven Explanations Robust against Out-of-distribution Data?" (CVPR 2023)
Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics
[ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
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 official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
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