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Found 6 papers, 2 papers with code
Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning
Conditioned on a target image, such methods extract the target style and combine it with the source image content, keeping coherence between the domains.
Image-to-Image Translation with Low Resolution Conditioning
This differs from previous methods that focus on translating a given image style into a target content, our translation approach being able to simultaneously imitate the style and merge the structural information of the LR target.
Ranked #5 on
Image-to-Image Translation
on CelebA-HQ
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
Traditionally, the main focus of image super-resolution techniques is on recovering the most likely high-quality images from low-quality images, using a one-to-one low- to high-resolution mapping.
Learning of Image Dehazing Models for Segmentation Tasks
To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth.
Accumulating Knowledge for Lifelong Online Learning
We are providing a theoretical analysis of this algorithm, with a cumulative error upper bound for each task.
Diversity regularization in deep ensembles
Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable.
