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Found 5 papers, 3 papers with code
Patch-wise Graph Contrastive Learning for Image Translation
Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images.
Self-supervised debiasing using low rank regularization
Specifically, we first pretrain a biased encoder in a self-supervised manner with the rank regularization, serving as a semantic bottleneck to enforce the encoder to learn the spuriously correlated attributes.
Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising
The acquisition conditions for low-dose and high-dose CT images are usually different, so that the shifts in the CT numbers often occur.
Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence.
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems
To improve the performance of classical generative adversarial network (GAN), Wasserstein generative adversarial networks (W-GAN) was developed as a Kantorovich dual formulation of the optimal transport (OT) problem using Wasserstein-1 distance.
