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pro vyhledávání: '"Jayendran, Aravind"'
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 generated data is well-clustered, especially when there is severe class
Externí odkaz:
http://arxiv.org/abs/2005.02435
Autor:
Mondal, Arnab Kumar, Chowdhury, Sankalan Pal, Jayendran, Aravind, Singla, Parag, Asnani, Himanshu, AP, Prathosh
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provide an a
Externí odkaz:
http://arxiv.org/abs/1912.04564
Generative adversarial networks (GANs) have shown remarkable success in generation of unstructured data, such as, natural images. However, discovery and separation of modes in the generated space, essential for several tasks beyond naive data generat
Externí odkaz:
http://arxiv.org/abs/1811.03692