Discriminative Adversarial Domain Generalization with Meta-learning
based Cross-domain Validation
- URL: http://arxiv.org/abs/2011.00444v2
- Date: Wed, 16 Feb 2022 01:23:31 GMT
- Title: Discriminative Adversarial Domain Generalization with Meta-learning
based Cross-domain Validation
- Authors: Keyu Chen, Di Zhuang, J. Morris Chang
- Abstract summary: Domain Generalization (DG) techniques aim to enhance such generalization capability of machine learning models.
We propose Discriminative Adversarial Domain Generalization (DADG) with meta-learning-based cross-domain validation.
Results show DADG consistently outperforms a strong baseline DeepAll, and outperforms the other existing DG algorithms in most of the evaluation cases.
- Score: 9.265557367859637
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 contains
two main components that work synergistically to build a domain-generalized 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. In the
experimental evaluation, a comprehensive comparison has been made among our
proposed approach and other existing approaches on three benchmark datasets.
The results shown that DADG consistently outperforms a strong baseline DeepAll,
and outperforms the other existing DG algorithms in most of the evaluation
cases.
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