Open AccessPosted Content
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
TL;DR: This work proposes a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN) and successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.
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Abstract: While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available this https URL
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Citations
Generating Manga from Illustrations via Mimicking Manga Creation Workflow
Lvmin Zhang,Xinrui Wang,Qingnan Fan,Yi Ji,Chunping Liu +4 more
- 01 Jun 2021
TL;DR: A data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screen-tone, and irregular screen texture is presented and it is observed that the generated image layers for these three components are practically usable in the daily works of manga artists.
Beyond Supervised Learning: A Computer Vision Perspective
TL;DR: An overview of the contemporary literature surrounding alternatives to fully supervised learning in the deep learning context is provided and the relevant techniques that fall between the paradigm of supervised and unsupervised learning are summarized.
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A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification
TL;DR: A unified network architecture of Multi-domain and Multi-modal Representation Disentangler, with the goal of learning domain-invariant content representation with the associated domain-specific representation observed, that can be applied for unsupervised domain adaptation.
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Patent
Cyclic generative adversarial network for unsupervised cross-domain image generation
Choi Wongun,Schulter Samuel,Sohn Kihyuk,Chandraker Manmohan +3 more
- 25 Oct 2018
TL;DR: In this paper, a cross-domain image generation system is proposed for unsupervised image generation relative to a first and second image domain that each include real images, where a first generator generates synthetic images similar to real images in the second domain while including a semantic content of real image in the first domain.
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Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment.
Shuhan Tan,Xingchao Peng,Kate Saenko +2 more
- 25 Sep 2019
TL;DR: This model leverages prototype-based conditional alignment and label distribution estimation to diminish the covariate and label shifts, respectively, and demonstrates experimentally that when both types of shift exist in the data, COAL leads to state-of-the-art performance on several cross-domain benchmarks.
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
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TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
•Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
- 01 Jan 2014
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
•Posted Content
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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