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Found 6 papers, 2 papers with code
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
In addition, we propose training a semantic segmentation network along with the translation task, and to leverage this output as a loss term that improves robustness.
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.
Single Pair Cross-Modality Super Resolution
The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.
Image Resizing by Reconstruction from Deep Features
In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information.
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets.
