Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification

• URL: http://arxiv.org/abs/2301.10611v3
• Date: Wed, 8 Nov 2023 13:29:57 GMT
• Title: Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
• Authors: Indel Pal Singh, Enjie Ghorbel, Anis Kacem, Arunkumar Rathinam and Djamila Aouada
• Abstract summary: A discriminator-free adversarial-based Unminator Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) is proposed. The proposed method is evaluated on several multi-label image datasets covering three different types of domain shift.
• Score: 11.825795835537324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods which rely on an additional discriminator subnet present one major shortcoming. The learning of domain-invariant features may harm their task-specific discriminative power, since the classification and discrimination tasks are decoupled. Herein, we propose to overcome this issue by introducing a novel adversarial critic that is directly deduced from the task-specific classifier. Specifically, a two-component Gaussian Mixture Model (GMM) is fitted on the source and target predictions in order to distinguish between two clusters. This allows extracting a Gaussian distribution for each component. The resulting Gaussian distributions are then used for formulating an adversarial loss based on a Frechet distance. The proposed method is evaluated on several multi-label image datasets covering three different types of domain shift. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods in terms of precision while requiring a lower number of parameters. The code is publicly available at github.com/cvi2snt/DDA-MLIC.

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