Why Data Augmentation Triggers OOD Fluctuations and How PEER Solves It

Data augmentation, while popular for single-source domain generalization, often induces severe out-of-distribution performance swings during training; the PEER framework combats this by employing dual-model collaboration, entropy regularization, periodic parameter averaging, and dynamic augmentation, achieving state-of-the-art robustness across multiple benchmark datasets.

OOD robustnessdata augmentationdomain generalization

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Why Data Augmentation Triggers OOD Fluctuations and How PEER Solves It

Kuaishou Tech

Apr 25, 2023 · Artificial Intelligence

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

The paper introduces DCCL, a model‑agnostic contrastive learning framework that decouples user interest and conformity representations to address popularity bias and out‑of‑distribution challenges in recommendation systems, demonstrating significant offline and online performance gains on real‑world datasets.

OOD robustnesscausal inferencecontrastive learning

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DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems