COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand... (read more)

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Methods used in the Paper


METHOD TYPE
Random Search
Hyperparameter Search
Leaky ReLU
Activation Functions
ReLU
Activation Functions
Batch Normalization
Normalization
DCGAN
Generative Models
Convolution
Convolutions
GAN
Generative Models