MixStyle Neural Networks for Domain Generalization and Adaptation

Article Computer Science, Artificial Intelligence

Batch normalization emb e ddings for deep domain generalization

Mattia Segu et al.

Summary: Domain generalization aims to train machine learning models that can perform robustly across different domains. This study proposes a new approach that explicitly trains domain-dependent representations and maps domains in a shared latent space, achieving better generalization performance.

PATTERN RECOGNITION (2023)

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Article Computer Science, Artificial Intelligence

Domain Generalization: A Survey

Kaiyang Zhou et al.

Summary: Domain generalization aims to achieve generalization to out-of-distribution data by using only source data for model learning. This paper provides a comprehensive literature review on the developments in domain generalization over the past decade, including the background, existing methods and theories, and insights and discussions on future research directions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

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Article Computer Science, Artificial Intelligence

Learning Generalisable Omni-Scale Representations for Person Re-Identification

Kaiyang Zhou et al.

Summary: In this paper, we propose an effective person re-identification (re-ID) model that can distinguish similar-looking people and can be deployed across datasets without any adaptation. The model consists of an omni-scale network (OSNet) for feature learning and instance normalisation (IN) layers for improving generalisation. Experimental results show that the proposed model outperforms existing re-ID models in terms of performance, both in the same-dataset and cross-dataset settings.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

A Strong Baseline for Vehicle Re-Identification

Su Huynh et al.

Summary: This paper analyzes the technical challenges and proposes solutions for Vehicle Re-Identification, achieving significant improvement on the 5th AI City Challenge Track 2 dataset. The method outperforms previous works on the Veri benchmark.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 (2021)

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Article Computer Science, Artificial Intelligence

Domain Adaptive Ensemble Learning

Kaiyang Zhou et al.

Summary: The study focuses on generalizing deep neural networks from multiple source domains to a target domain, and proposes a unified framework called DAEL, which aims to improve accuracy on unseen target domains by collaboratively learning experts.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

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Article Computer Science, Artificial Intelligence