Batch normalization embeddings for deep domain generalization. (March 2023)

• Record Type: Journal Article
• Authors: Segu, Mattia; Tonioni, Alessio; Tombari, Federico
• Is Part Of: Pattern recognition. Volume 135(2023)
• Publication Date: 2023-03
• Subjects: Domain generalization -- Domain representation learning -- Learning from multiple sources; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109115
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose to accumulate domain-specific batch normalization statistics accumulated on convolutional layers to map image samples into a latent space where membership to a domain can be measured according to a distance from domain batch population statistics We propose to use this concept to learn a lightweight ensemble model that shares all parameters excepts the normalization statistics and can generalize better to unseen domains Compared to previous work, we do not discard domain-specific attributes but exploit them to learn a domain latent space and map unknown domains with respect to known ones We show a significant increase in classification accuracy over current state-of-the-art techniques on popular domain generalization benchmarks: PACS, Office-31 and Office-Caltech. Graphical abstract: Abstract: Domain generalization aims at training machine learning models to perform robustly across different and unseen domains. Several methods train models from multiple datasets to extract domain-invariant features, hoping to generalize to unseen domains. Instead, first we explicitly train domain-dependent representations leveraging ad-hoc batch normalization layers to collect independent domain's statistics. Then, we propose to use these statistics to map domains in a shared latent space, where membership to a domain is measured by means of a distance function. At test time, we project samples from an unknown domain into the same space and infer properties of their domain as a linear combination of the known ones. We apply the same mapping strategy at training and test time, learning both a latent representation and a powerful but lightweight ensemble model. We show a significant increase in classification accuracy over current state-of-the-art techniques on popular domain generalization benchmarks: PACS, Office-31 and Office-Caltech.