201 citations · 718 across the 30 of their papers we have counts for
5 papers · 1 filter
Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
Francisco Sedeño, Francisco Chicano, Jamal Toutouh
Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unsta…
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks
Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial intera…
Data Dieting in GAN Training
Jamal Toutouh, Una-May O'Reilly, Erik Hemberg
We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resour…
DLOPT: Deep Learning Optimization Library
Andrés Camero, Jamal Toutouh, Enrique Alba
Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done.…
Low-Cost Recurrent Neural Network Expected Performance Evaluation
Andrés Camero, Jamal Toutouh, Enrique Alba
Recurrent neural networks are a powerful tool, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough…