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
Posted Content
Intensity augmentation for domain transfer of whole breast segmentation in MRI.
TL;DR: The results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and the expect it to be useful for a wide range of segmentation tasks.
Proceedings ArticleDOI
Learning Shape-based Representation for Visual Localization in Extremely Changing Conditions
TL;DR: This paper presents a convolutional neural network (CNN)-based approach for visual localization across normal to drastic appearance variations such as pre- and post-disaster cases and shows that the method provides reliable camera pose predictions in various changing conditions.
Posted Content
SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal
Daichi Horita,Kiyoharu Aizawa +1 more
TL;DR: This paper provides a novel, perceptual makeup loss and a style-invariant decoder that can transfer makeup styles based on histogram matching to avoid the identity-shift problem and shows that the SLGAN is better than or comparable to state-of-the-art methods.
Posted Content
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
TL;DR: In this article, the authors propose a new approach for synthesizing fully detailed art-stylized images from sketches, which consists of three modules designed explicitly for better artistic style capturing and generation.
Posted Content
Depth-Preserving Real-Time Arbitrary Style Transfer.
TL;DR: Qualitative analysis and results of user evaluation study indicate that the proposed AdaIN method provides better stylizations, compared to the original style transfer methods of Gatys et al. (2016) and Huang etal.
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.