#5808. Discriminative adversarial domain generalization with meta-learning based cross-domain validation

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Cognitive Neuroscience;
Computer Science Applications;
Artificial Intelligence;
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Abstract:
The generalization capability of machine learning models, which refers to generalizing the knowledge for an “unseen” domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions. Domain Generalization (DG) techniques aim to enhance such generalization capability of machine learning models, where the learnt feature representation and the classifier are two crucial factors to improve generalization and make decisions. In this paper, we propose Discriminative Adversarial Domain Generalization (DADG) with meta-learning-based cross-domain validation. Our proposed framework tries to learn a domain-invariant feature representation from source domains and generalize it to the unseen domains. It contains two main components that work synergistically to build a domain-generalized Deep Neural Network (DNN) model: (i) discriminative adversarial learning, which proactively learns a generalized feature representation on multiple “seen” domains, and (ii) meta-learning based cross domain validation, which simulates train/test domain shift via applying meta-learning techniques in the training process.
Keywords:
Convolutional neural network; Discriminative adversarial learning; Domain generalization; Meta-learning

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