DLOW: Domain Flow for Adaptation and Generalization
Rui Gong,Wen Li,Yuhua Chen,Luc Van Gool +3 more
- pp 2472-2481
TLDR
A domain flow generation model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other and demonstrating the effectiveness of the model for both cross-domain semantic segmentation and the style generalization tasks on benchmark datasets is presented.Abstract:
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other. The benefits of our DLOW model are two-fold. First, it is able to transfer source images into different styles in the intermediate domains. The transferred images smoothly bridge the gap between source and target domains, thus easing the domain adaptation task. Second, when multiple target domains are provided for training, our DLOW model is also able to generate new styles of images that are unseen in the training data. We implement our DLOW model based on CycleGAN. A domainness variable is introduced to guide the model to generate the desired intermediate domain images. In the inference phase, a flow of various styles of images can be obtained by varying the domainness variable. We demonstrate the effectiveness of our model for both cross-domain semantic segmentation and the style generalization tasks on benchmark datasets. Our implementation is available at https://github.com/ETHRuiGong/DLOW .read more
Citations
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Proceedings ArticleDOI
Confidence Regularized Self-Training
TL;DR: Zhou et al. as discussed by the authors proposed a confidence regularized self-training (CRST) framework, which treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization.
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Confidence Regularized Self-Training.
TL;DR: A confidence regularized self-training (CRST) framework, formulated as regularizedSelf-training, that treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization and proposes two types of confidence regularization: label regularization (LR) and modelRegularization (MR).
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FDA: Fourier Domain Adaptation for Semantic Segmentation
Yanchao Yang,Stefano Soatto +1 more
TL;DR: A simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other, which results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.
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FDA: Fourier Domain Adaptation for Semantic Segmentation
Yanchao Yang,Stefano Soatto +1 more
TL;DR: In this paper, the Fourier Transform and its inverse is used for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other.
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Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
TL;DR: ProDA as mentioned in this paper aligns the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space and distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance.
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