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cs.LG2023
Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks
Piyush Tiwary, Atri Guha, Subhodip Panda +1
Owing to the growing concerns about privacy and regulatory compliance, it is desirable to regulate the output of generative models. To that end, the objective of this work is to pr…
cs.LG2020★ 13 cited
Effect of The Latent Structure on Clustering with GANs
Deepak Mishra, Aravind Jayendran, Prathosh A. P
Generative adversarial networks (GANs) have shown remarkable success in generation of data from natural data manifolds such as images. In several scenarios, it is desirable that ge…