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Domain Generalization via Model-Agnostic Learning of Semantic Features
Qi Dou · Daniel Coelho de Castro · Konstantinos Kamnitsas · Ben Glocker

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #128

Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge of inter-class relationships. Locally, we promote domain-independent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task.

Author Information

Qi Dou (Imperial College London)

Dr. Qi DOU is a postdoctoral research associate at the Department of Computing at Imperial College London. Before that, she has received her Ph.D. degree in Computer Science and Engineering at The Chinese University of Hong Kong in July 2018. Her research interests are in the development of advanced machine learning methods for healthcare applications with specifics to medical image computing. Dr. Dou has won the Best Paper Award of Medical Image Analysis-MICCAI in 2017, the Best Paper Award of Medical Imaging and Augmented Reality in 2016, and MICCAI Young Scientist Award Runner-up in 2016. She has also won the HKIS Young Scientist Award 2018. She is going to join CUHK as an Assistant Professor in Jan 2020.

Daniel Coelho de Castro (Imperial College London)
Konstantinos Kamnitsas (Imperial College London)
Ben Glocker (Imperial College London)

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