1,558 research outputs found
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI
2018
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
We present DeblurGAN, an end-to-end learned method for motion deblurring. The
learning is based on a conditional GAN and the content loss . DeblurGAN
achieves state-of-the art performance both in the structural similarity measure
and visual appearance. The quality of the deblurring model is also evaluated in
a novel way on a real-world problem -- object detection on (de-)blurred images.
The method is 5 times faster than the closest competitor -- DeepDeblur. We also
introduce a novel method for generating synthetic motion blurred images from
sharp ones, allowing realistic dataset augmentation.
The model, code and the dataset are available at
https://github.com/KupynOrest/DeblurGANComment: CVPR 2018 camera-read
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