Domain-aware Triplet loss in Domain Generalization

1 Mar 2023  ·  Kaiyu Guo, Brian Lovell ·

Despite much progress being made in the field of object recognition with the advances of deep learning, there are still several factors negatively affecting the performance of deep learning models. Domain shift is one of these factors and is caused by discrepancies in the distributions of the testing and training data. In this paper, we focus on the problem of compact feature clustering in domain generalization to help optimize the embedding space from multi-domain data. We design a domainaware triplet loss for domain generalization to help the model to not only cluster similar semantic features, but also to disperse features arising from the domain. Unlike previous methods focusing on distribution alignment, our algorithm is designed to disperse domain information in the embedding space. The basic idea is motivated based on the assumption that embedding features can be clustered based on domain information, which is mathematically and empirically supported in this paper. In addition, during our exploration of feature clustering in domain generalization, we note that factors affecting the convergence of metric learning loss in domain generalization are more important than the pre-defined domains. To solve this issue, we utilize two methods to normalize the embedding space, reducing the internal covariate shift of the embedding features. The ablation study demonstrates the effectiveness of our algorithm. Moreover, the experiments on the benchmark datasets, including PACS, VLCS and Office-Home, show that our method outperforms related methods focusing on domain discrepancy. In particular, our results on RegnetY-16 are significantly better than state-of-the-art methods on the benchmark datasets. Our code will be released at https://github.com/workerbcd/DCT

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Domain Generalization Office-Home D-Triplet(RegNetY-16GF) Average Accuracy 82.6 # 9
Domain Generalization Office-Home D-Triplet(Resnet-50) Average Accuracy 70.3 # 24
Domain Generalization PACS D-Triplet(RegNetY-16GF) Average Accuracy 97.6 # 3
Domain Generalization PACS D-Triplet(Resnet-50) Average Accuracy 87.3 # 30
Domain Generalization VLCS D-Triplet(RegNetY-16GF) Average Accuracy 82.9 # 3
Domain Generalization VLCS D-Triplet(Resnet-50) Average Accuracy 79.3 # 19

Methods