Semi-Supervised Learning for Few-Shot Image-to-Image Translation

@article{Wang2020SemiSupervisedLF,
  title={Semi-Supervised Learning for Few-Shot Image-to-Image Translation},
  author={Yaxing Wang and Salman Hameed Khan and Abel Gonzalez-Garcia and Joost van de Weijer and Fahad Shahbaz Khan},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={4452-4461}
}
  • Yaxing WangS. Khan F. Khan
  • Published 30 March 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In the last few years, unpaired image-to-image translation has witnessed Remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image ranslation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the… 
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26 Citations
Highly Influential Citations
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26 Citations

Rethinking the Truly Unsupervised Image-to-Image Translation

A truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains and is robust against the choice of hyperparameters is proposed.

ManiFest: Manifold Deformation for Few-shot Image Translation

ManiFest is a framework for few-shot image translation that learns a context-aware representation of a target domain from a few images only, and can alternatively be conditioned on a single exemplar image to reproduce its specific style.

LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

A LANguage-driven Image-to-image Translation model, dubbed LANIT, that achieves comparable or superior performance to existing models and introduces a slack domain to cover samples that are not covered by the candidate domains.

Leveraging Local Domains for Image-to-Image Translation

This paper leverages human knowledge about spatial domain characteristics which it refers to as ’local domains’ and demonstrates its benevolence for image-to-image translation and shows that all tested proxy tasks are significantly improved, without ever seeing target domain at training.

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

A novel deep hierarchical Image-to-Image Translation method, called DeepI2I, that transfers knowledge from pre-trained GANs and qualitatively and quantitatively demonstrates that transfer learning significantly improves the performance of I2I systems, especially for small datasets.

Attribute Group Editing for Reliable Few-shot Image Generation

A new “editing-based” method, i.e., Attribute Group Editing (AGE), for few-shot image generation, capable of not only producing more realistic and diverse images for downstream visual applications with limited data but achieving controllable image editing with interpretable category-irrelevant directions.

Few-shot Semantic Image Synthesis Using StyleGAN Prior

This paper presents a training strategy that performs pseudo labeling of semantic masks using the StyleGAN prior that can synthesize high-quality images from not only dense semantic masks but also sparse inputs such as landmarks and scribbles.

Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal

The FSMA framework is prominent in its versatility across a wide range of facial image applications and achieves state-of-the-art few-shot landmark detection performance and it offers satisfying solutions for few- shot face segmentation, stylization and facial shadow removal tasks for the first time.

Local Propagation for Few-Shot Learning

This work treats local image features as independent examples, builds a graph on them and uses it to propagate both the features themselves and the labels, known and unknown, improving accuracy over corresponding methods.

Multi-Style Unsupervised Image Synthesis Using Generative Adversarial Nets

A novel Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks.

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