all AI news
A Wasserstein perspective of Vanilla GANs
March 25, 2024, 4:42 a.m. | Lea Kunkel, Mathias Trabs
cs.LG updates on arXiv.org arxiv.org
Abstract: The empirical success of Generative Adversarial Networks (GANs) caused an increasing interest in theoretical research. The statistical literature is mainly focused on Wasserstein GANs and generalizations thereof, which especially allow for good dimension reduction properties. Statistical results for Vanilla GANs, the original optimization problem, are still rather limited and require assumptions such as smooth activation functions and equal dimensions of the latent space and the ambient space. To bridge this gap, we draw a connection …
abstract adversarial arxiv cs.lg gans generative generative adversarial networks good literature math.st networks optimization perspective research results statistical stat.ml stat.th success type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer
@ GPTZero | Toronto, Canada
ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)
@ HelloBetter | Remote
Doctoral Researcher (m/f/div) in Automated Processing of Bioimages
@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena
Director, Global Success Business Intelligence
@ Salesforce | Texas - Austin
Deep Learning Compiler Engineer - MLIR
@ NVIDIA | US, CA, Santa Clara
Commerce Data Engineer (Remote)
@ CrowdStrike | USA TX Remote