Open AccessProceedings Article
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.
Xun Huang,Serge Belongie +1 more
Reads0
Chats0
TLDR
In this article, adaptive instance normalization (AdaIN) is proposed to align the mean and variance of the content features with those of the style features, which enables arbitrary style transfer in real-time.Abstract:
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.read more
Citations
More filters
Journal ArticleDOI
Exemplar-Based Portrait Style Transfer
TL;DR: This paper proposes a robust approach for portrait style transfer that gets rid of dense correspondence based on the guided image synthesis framework and proposes three novel guidance maps for the synthesis process that allow this method to handle the whole portrait photo instead of facial region only.
Posted Content
Learning to Caricature via Semantic Shape Transform
Wenqing Chu,Wenqing Chu,Wei-Chih Hung,Yi-Hsuan Tsai,Yu-Ting Chang,Yijun Li,Deng Cai,Ming-Hsuan Yang,Ming-Hsuan Yang +8 more
TL;DR: This work proposes an algorithm based on a semantic shape transform to produce diverse and plausible shape exaggerations to deal with large shape changes and allows users to manipulate the shape via the semantic map.
Posted Content
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising
Jawook Gu,Jong Chul Ye +1 more
TL;DR: Experimental results show that the proposed method outperforms the previous cycleGAN approaches while using only about half the parameters, and the training can be done more stably even with small training data.
Posted Content
An Unpaired Sketch-to-Photo Translation Model.
Runtao Liu,Qian Yu,Stella X. Yu +2 more
TL;DR: This work shows that the key to this task lies in decomposing the translation into two subtasks, shape translation and colorization, and proposes a model consisting of two sub-networks, with each one tackling one sub-task.
Proceedings ArticleDOI
Spatial Content Alignment for Pose Transfer
TL;DR: This paper proposes a novel framework – Spatial Content Alignment GAN (SCA-GAN) which aims to enhance the content consistency of garment textures and the details of human characteristics and introduces a new Content-Style DeBlk which can progressively synthesize photo-realistic person images.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
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.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.