mlsanityData to 7 Oct 2026

Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data

arXiv 2102.10485 ↗First posted 21 Feb 2021cs.CV cs.AIcited by 1

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Abstract (arXiv)

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 training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1,000 processes and 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 21 Feb 2021.

Builds on

33 of 42 references.

References · 33

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21 more
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  1. 2022Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networkscited by 0