SCGN: novel generative model using the convergence of latent space by training. Issue 17 (1st August 2020)

• Record Type: Journal Article
• Authors: Kim, H.; Jung, S.H.
• Is Part Of: Electronics letters. Volume 56:Issue 17(2020)
• Page Start: 879
• Page End: 881
• Publication Date: 2020-08-01
• Subjects: learning (artificial intelligence) -- neural nets -- image resolution; mode collapse -- generative model -- self‐converging generative network -- SCGN -- self‐converging means -- latent vectors -- training data -- authors -- pixel‐wise loss -- GAN -- latent space -- generative adversarial networks -- VAE -- Kullback–Leibler divergence; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1333
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have been recently applied to various fields. However, the VAE and GAN models have blur and mode collapse problems, respectively. Here, the authors propose a novel generative model, self‐converging generative network (SCGN), to address the issues. Self‐converging means the convergence of latent vectors into themselves through being trained in pairs with training data, by which the SCGN can reconstruct all training data. In the authors' model, the latent vectors and weights of the generator are alternately trained. Specifically, the latent vectors are trained to follow a normal distribution, using a loss function derived from the Kullback–Leibler divergence and a pixel‐wise loss. The weights of the generator are adjusted for the generator to produce training data by means of a pixel‐wise loss. As a result, their SCGN did not fall into the mode collapse, which occurs in GANs, and made clearer images than VAEs thanks to no use of sampling. Moreover, the SCGN successfully learned the manifold of the dataset in the extensive experiments with CelebA.