Search Results for author: Geonho Hwang

Found 7 papers, 1 papers with code

Minimum Width for Deep, Narrow MLP: A Diffeomorphism and the Whitney Embedding Theorem Approach

no code implementations30 Aug 2023 Geonho Hwang

Recently, there has been significant attention on determining the minimum width for the universal approximation property of deep, narrow MLPs.

Minimal Width for Universal Property of Deep RNN

no code implementations25 Nov 2022 Chang hoon Song, Geonho Hwang, Jun Ho Lee, Myungjoo Kang

In this study, we prove the universality of deep narrow RNNs and show that the upper bound of the minimum width for universality can be independent of the length of the data.

Universal Property of Convolutional Neural Networks

no code implementations18 Nov 2022 Geonho Hwang, Myungjoo Kang

A convolution with padding outputs the data of the same shape as the input data; therefore, it is necessary to prove whether a convolutional neural network composed of convolutions can approximate such a function.

Finding the global semantic representation in GAN through Frechet Mean

no code implementations11 Oct 2022 Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang

This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space.

Analyzing the Latent Space of GAN through Local Dimension Estimation

no code implementations26 May 2022 Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang

In this paper, we approach this problem through a geometric analysis of latent spaces as a manifold.

Disentanglement Image Generation

Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs

2 code implementations ICLR 2022 Jaewoong Choi, Junho Lee, Changyeon Yoon, Jung Ho Park, Geonho Hwang, Myungjoo Kang

The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.

Disentanglement Image Generation +1

Discond-VAE: Disentangling Continuous Factors from the Discrete

no code implementations17 Sep 2020 Jaewoong Choi, Geonho Hwang, Myungjoo Kang

To represent these generative factors of data, we introduce two sets of continuous latent variables, private variable and public variable.


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