Image synthesis with adversarial networks: A comprehensive survey and case studies
Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …
various application domains such as computer vision, medicine, and natural language …
Euler-Lagrange analysis of generative adversarial networks
S Asokan, CS Seelamantula - Journal of Machine Learning Research, 2023 - jmlr.org
We consider Generative Adversarial Networks (GANs) and address the underlying
functional optimization problem ab initio within a variational setting. Strictly speaking, the …
functional optimization problem ab initio within a variational setting. Strictly speaking, the …
A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy
Three-dimensional information is crucial to our understanding of biological phenomena. The
vast majority of biological microscopy specimens are inherently three-dimensional …
vast majority of biological microscopy specimens are inherently three-dimensional …
Express Construction for GANs from Latent Representation to Data Distribution
Featured Application This concise is a novel training methodology for GANs with strong
generalization ability, speed the training up, and less prone to mode collapse. Abstract …
generalization ability, speed the training up, and less prone to mode collapse. Abstract …
Optimal transport based generative autoencoders
The field of deep generative modeling is dominated by generative adversarial networks
(GANs). However, the training of GANs often lacks stability, fails to converge, and suffers …
(GANs). However, the training of GANs often lacks stability, fails to converge, and suffers …