Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis

• URL: http://arxiv.org/abs/2402.16090v1
• Date: Sun, 25 Feb 2024 13:37:36 GMT
• Title: Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
• Authors: Andrea Maracani, Raffaello Camoriano, Elisa Maiettini, Davide Talon, Lorenzo Rosasco and Lorenzo Natale
• Abstract summary: This study provides a benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification. The study empirically examines a diverse set of SF-UDA techniques, assessing their consistency across datasets. It exhaustively evaluates pre-training datasets and strategies, particularly focusing on both supervised and self-supervised methods.
• Score: 16.0130560365211
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods. The study empirically examines a diverse set of SF-UDA techniques, assessing their consistency across datasets, sensitivity to specific hyperparameters, and applicability across different families of backbone architectures. Moreover, it exhaustively evaluates pre-training datasets and strategies, particularly focusing on both supervised and self-supervised methods, as well as the impact of fine-tuning on the source domain. Our analysis also highlights gaps in existing benchmark practices, guiding SF-UDA research towards more effective and general approaches. It emphasizes the importance of backbone architecture and pre-training dataset selection on SF-UDA performance, serving as an essential reference and providing key insights. Lastly, we release the source code of our experimental framework. This facilitates the construction, training, and testing of SF-UDA methods, enabling systematic large-scale experimental analysis and supporting further research efforts in this field.

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