Randomized Adversarial Style Perturbations for Domain Generalization
- URL: http://arxiv.org/abs/2304.01959v2
- Date: Tue, 21 Nov 2023 06:04:48 GMT
- Title: Randomized Adversarial Style Perturbations for Domain Generalization
- Authors: Taehoon Kim, Bohyung Han
- Abstract summary: We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP)
The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly selected class, and makes the model learn against being misled by the unexpected styles observed in unseen target domains.
We evaluate the proposed algorithm via extensive experiments on various benchmarks and show that our approach improves domain generalization performance, especially in large-scale benchmarks.
- Score: 49.888364462991234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel domain generalization technique, referred to as Randomized
Adversarial Style Perturbation (RASP), which is motivated by the observation
that the characteristics of each domain are captured by the feature statistics
corresponding to style. The proposed algorithm perturbs the style of a feature
in an adversarial direction towards a randomly selected class, and makes the
model learn against being misled by the unexpected styles observed in unseen
target domains. While RASP is effective to handle domain shifts, its naive
integration into the training procedure might degrade the capability of
learning knowledge from source domains because it has no restriction on the
perturbations of representations. This challenge is alleviated by Normalized
Feature Mixup (NFM), which facilitates the learning of the original features
while achieving robustness to perturbed representations via their mixup during
training. We evaluate the proposed algorithm via extensive experiments on
various benchmarks and show that our approach improves domain generalization
performance, especially in large-scale benchmarks.
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