Generative Adversarial Stacked Autoencoders

• URL: http://arxiv.org/abs/2011.12236v1
• Date: Sun, 22 Nov 2020 17:51:59 GMT
• Title: Generative Adversarial Stacked Autoencoders
• Authors: Ariel Ruiz-Garcia, Ibrahim Almakky, Vasile Palade, Luke Hicks
• Abstract summary: We propose a Generative Adversarial Stacked Convolutional Autoencoder(GASCA) model and a generative adversarial gradual greedy layer-wise learning algorithm de-signed to train Adversarial Autoencoders. Our training approach produces images with significantly lower reconstruction error than vanilla joint training.
• Score: 3.1829446824051195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Adversarial Networks (GANs) have become predominant in image generation tasks. Their success is attributed to the training regime which employs two models: a generator G and discriminator D that compete in a minimax zero sum game. Nonetheless, GANs are difficult to train due to their sensitivity to hyperparameter and parameter initialisation, which often leads to vanishing gradients, non-convergence, or mode collapse, where the generator is unable to create samples with different variations. In this work, we propose a novel Generative Adversarial Stacked Convolutional Autoencoder(GASCA) model and a generative adversarial gradual greedy layer-wise learning algorithm de-signed to train Adversarial Autoencoders in an efficient and incremental manner. Our training approach produces images with significantly lower reconstruction error than vanilla joint training.

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