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Found 7 papers, 3 papers with code
PhenDiff: Revealing Invisible Phenotypes with Conditional Diffusion Models
Furthermore, the lack of robustness to invert a real image into the latent of a trained GAN prevents flexible editing of real images.
Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off
Recent unsupervised approaches search the latent space of a StyleGAN pre-trained on HR images, for the image that best downscales to the input LR image.
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment.
One Style is All you Need to Generate a Video
In this paper, we propose a style-based conditional video generative model.
No Free Lunch in Self Supervised Representation Learning
In this work, we explore this relationship, its impact on a domain other than natural images, and show that designing the transformations can be viewed as a form of supervision.
Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes
Unpaired image-to-image translation methods aim at learning a mapping of images from a source domain to a target domain.
Comparison of semi-supervised learning methods for High Content Screening quality control
In this work, we evaluate deep learning options that do not require extensive image annotations to provide a straightforward and easy to use semi-supervised learning solution to this issue.
