Artificial Intelligence


Acl-Gan: Multi-Domain Image-to-Image Translation Gan Using New Losses to Reduce the Time for Hyperparameter Optimization and Training

Authors: JeongIk Cho

StarGAN, which has impressive performance in image-to-image translation, is based on the determination of three important hyperparameters: adversarial weight, classification weight, and reconstruction weight, which have a significant impact on the performance of the model. In this study, by proposing an attribute loss that can replace conditional GAN losses: adversarial loss and classification loss, the time required for the optimization of attribute weight replaced the time required for the optimization of adversarial weight and classification weight, which can drastically reduce the time required for hyperparameter optimization. Proposed attribute loss is the sum of the losses of each GAN when creating a GAN for each attribute, and since each GAN shares a hidden layer, it does not increase the amount of computation much. Also, propose simplified content loss, which reduces computation by simplifying reconstruction loss. Reconstruction loss of StarGAN goes through the generator twice, while simplified content loss goes through only once, reduce the amount of computation. Also, propose an architecture that prevents background distortion through image framing and improves training speed through a bidirectional progressive growing generator.

Comments: 13 Pages.

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Submission history

[v1] 2019-09-03 20:56:00
[v2] 2019-10-05 10:38:22
[v3] 2019-11-05 18:35:57
[v4] 2019-11-19 21:46:23

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