Scalable balanced training of conditional generative adversarial neural networks on image data

We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel tr...

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Veröffentlicht in:	The Journal of supercomputing 2021-11, Vol.77 (11), p.13358-13384
Hauptverfasser:	Lupo Pasini, Massimiliano, Gabbi, Vittorio, Yin, Junqi, Perotto, Simona, Laanait, Nouamane
Format:	Artikel
Sprache:	eng
Schlagworte:	Compilers Computer Science Computer vision Datasets Deep learning Generative adversarial neural networks Image quality Interpreters MATHEMATICS AND COMPUTING Neural networks Processor Architectures Programming Languages Supercomputing Training
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