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UniPET is a universal network for PET image denoising that handles varying dose reduction factors using domain generalization and region-aware learning, achieving state-of-the-art performance.
This paper investigates whether code-switching ASR capabilities learned from limited seen language pairs can generalize to unseen pairs using model merging and domain generalization methods, finding only modest transfer.
This paper introduces Domain Generalizable Dataset Distillation (DGDD), a new problem setting that targets out-of-distribution generalization of distilled datasets, and proposes Spectral Gradient Surgery (SGS) to disentangle class-discriminative and domain-specific information by leveraging cross-domain gradient agreement in the spectral domain.
CPCANet is a domain generalization framework that uses Common Principal Component Analysis to discover structured domain-invariant subspaces, achieving state-of-the-art performance in zero-shot transfer.
This paper introduces MMDG-Bench, a unified benchmark for multimodal domain generalization that reveals limited progress in current methods and significant robustness challenges across diverse tasks.