/ Thu, 13 Jul 2023 16:50:56 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.4 /unsupervised-robust-domain-adaptation-without-source-data/ Tue, 04 Jan 2022 20:39:34 +0000 /?p=123

Peshal Agarwal, Danda Pani Paudel, Jan-Nico Zaech, Luc Van Gool

Winter Conference on Applications of Computer Vision, WACV 2022

Abstract

We study the problem of robust domain adaptation in the context of unavailable target labels and source data. The considered robustness is against adversarial perturbations. This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data. The major findings of this paper are:(i) robust source models can be transferred robustly to the target;(ii) robust domain adaptation can greatly benefit from non-robust pseudo-labels and the pair-wise contrastive loss. The proposed method of using non-robust pseudo-labels performs surprisingly well on both clean and adversarial samples, for the task of image classification. We show a consistent performance improvement of over 10% in accuracy against the tested baselines on four benchmark datasets.

]]> /texture-underfitting-for-domain-adaptation/ Mon, 28 Oct 2019 19:05:45 +0000 /?p=150

Jan-Nico Zaech, Dengxin Dai, Martin Hahner, Luc Van Gool

Inteligent Transportation Systems Conference (IEEE), ITSC 2019

Abstract

Comprehensive semantic segmentation is one of the key components for robust scene understanding and a
requirement to enable autonomous driving. Driven by large scale datasets, convolutional neural networks show impressive results on this task. However, a segmentation algorithm generalizing to various scenes and conditions would require an enormously diverse dataset, making the labour intensive data acquisition and labeling process prohibitively expensive. Under the assumption of structural similarities between segmentation
maps, domain adaptation promises to resolve this challenge by transferring knowledge from existing, potentially simulated datasets to new environments where no supervision exists. While the performance of this approach is contingent on the concept that neural networks learn a high level understanding of scene structure, recent work suggests that neural networks are biased towards overfitting to texture instead of learning structural and shape information. Considering the ideas underlying semantic segmentation, we employ random image stylization to augment the training dataset and propose a training procedure that facilitates texture underfitting to improve the performance of domain adaptation. In experiments with supervised as well as unsupervised methods for the task of synthetic-to-real domain adaptation, we show that our approach outperforms conventional training methods.

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