Image-to-Image Translation: Methods and Applications
arXiv:2101.08629
Abstract
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, segmentation, style transfer, restoration, and pose estimation. In this paper, we provide an overview of the I2I works developed in recent years. We will analyze the key techniques of the existing I2I works and clarify the main progress the community has made. Additionally, we will elaborate on the effect of I2I on the research and industry community and point out remaining challenges in related fields.
arXiv comment24 pages, 21 figures
References in corpus (44)
- Conditional Generative Adversarial Nets
- Prototypical Networks for Few-shot Learning
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Spectral Normalization for Generative Adversarial Networks
- Conditional Image Generation with PixelCNN Decoders
- Pixel Recurrent Neural Networks
- NICE: Non-linear Independent Components Estimation
- Variational Inference with Normalizing Flows
- NIPS 2016 Tutorial: Generative Adversarial Networks
- Toward Multimodal Image-to-Image Translation
- Energy-based Generative Adversarial Network
- BEGAN: Boundary Equilibrium Generative Adversarial Networks
- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
- Adversarial Feature Learning
- Pose Guided Person Image Generation
- Disentangling by Factorising
- Adversarially Learned Inference
- Semi-Supervised Learning with Ladder Networks
- U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
- MADE: Masked Autoencoder for Distribution Estimation
- Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
- Unsupervised Attention-guided Image to Image Translation
- A survey on Semi-, Self- and Unsupervised Learning for Image Classification
- DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
- Swapping Autoencoder for Deep Image Manipulation
- One-Shot Unsupervised Cross Domain Translation
- A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
- InstaGAN: Instance-aware Image-to-Image Translation
- Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
- RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
- Bipartite Graph Reasoning GANs for Person Image Generation
- Few-shot Image Generation with Elastic Weight Consolidation
- MRI Cross-Modality NeuroImage-to-NeuroImage Translation
- DiDA: Disentangled Synthesis for Domain Adaptation
- Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
- GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
- Harmonic Unpaired Image-to-image Translation
- DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)
- Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning
- Cross-Domain Cascaded Deep Feature Translation
- ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
- High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network
- Powers of layers for image-to-image translation
- Learning to Transfer: Unsupervised Meta Domain Translation