Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
arXiv:1805.09987
Abstract
We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs. Besides the traditional content and style representation based on deep features and statistics for textures, we use adversarial networks to regularize the generation of stylized images. Our adversarial network learns the intrinsic property of image styles from large-scale multi-domain artistic images. The adversarial training is challenging because both the input and output of our generator are diverse multi-domain images. We use a conditional generator that stylized content by shifting the statistics of deep features, and a conditional discriminator based on the coarse category of styles. Moreover, we propose a mask module to spatially decide the stylization level and stabilize adversarial training by avoiding mode collapse. As a side effect, our trained discriminator can be applied to rank and select representative stylized images. We qualitatively and quantitatively evaluate the proposed method, and compare with recent style transfer methods. We release our code and model at https://github.com/nightldj/behance_release.
arXiv commentUpdate code link
References in corpus (19)
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Toward Multimodal Image-to-Image Translation
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
- Unsupervised Image-to-Image Translation Networks
- Texture Synthesis Using Convolutional Neural Networks
- Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
- CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
- Fast Patch-based Style Transfer of Arbitrary Style
- Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
- Multi-style Generative Network for Real-time Transfer
- Demystifying Neural Style Transfer
- StyleBank: An Explicit Representation for Neural Image Style Transfer
- Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
- Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
- Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
- Arbitrary Style Transfer with Deep Feature Reshuffle