yfzhang114/Generalization-Causality

# Technical Summary Curated reading notes repository tracking domain generalization, out-of-distribution detection, test-time adaptation, and causality research with organized paper summaries and implementation links. The collection emphasizes causal invariance principles and multi-domain optimization techniques, covering domain adversarial training, non-parametric classifiers for adaptation, and disentangled representation learning approaches. Integrates findings across vision (CVPR, ICLR, NeurIPS) and time-series forecasting domains, with code implementations and personal analysis notes primarily targeting PyTorch-based computer vision and ML safety applications.

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