Invariant Batch Normalization for Multi-source Domain Generalization
We consider the domain generalization problem, where the test domain differs from the training domain. For deep neural networks, we show that the batch normalization layer is a highly unstable component under such domain shifts, and we identify two sources for its instability. Based on this observation, we propose a new learning formulation that can learn robust neural networks so that the corresponding batch normalization layers are invariant under domain shifts. Experimental results on three standard domain generalization benchmarks demonstrate that our method can learn neural network models with significantly more stable batch normalization layers on unseen domains, and the improved stability leads to superior generalization performances.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain GeneralizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutiona…
Domain AdaptationUnsupervised Domain AdaptationDomain generalization Person Re-identification on Attention-aware multi-operation strategery
Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practica…
Domain GeneralizationPerson Re-IdentificationHistopathology Image Normalization via Latent Manifold Compaction
Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge for computational pathology, hindering cross-batch generalization and …
Representation LearningImage HarmonizationSPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However, its utility is limited as current EEG te…
Brain Computer InterfaceDomain AdaptationEEGElectroencephalogram (EEG)+2Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification
Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time, which is a realistic but challenging problem. In contrast to methods…
Generalizable Person Re-identificationMixture-of-ExpertsPerson Re-IdentificationUnsupervised Domain Adaptation