3 citations · 7 across the 4 of their papers we have counts for
5 papers
Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors
Andriy Serdega, Dae-Shik Kim
Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directe…
VMI-VAE: Variational Mutual Information Maximization Framework for VAE With Discrete and Continuous Priors
Andriy Serdega, Dae-Shik Kim
Variational Autoencoder is a scalable method for learning latent variable models of complex data. It employs a clear objective that can be easily optimized. However, it does not ex…
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding
Geondo Park, Chihye Han, Wonjun Yoon +1
Visual-semantic embedding enables various tasks such as image-text retrieval, image captioning, and visual question answering. The key to successful visual-semantic embedding is to…
Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
Gihyun Kwon, Chihye Han, Dae-shik Kim
As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to s…
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study
Chihye Han, Wonjun Yoon, Gihyun Kwon +2
The recent success of brain-inspired deep neural networks (DNNs) in solving complex, high-level visual tasks has led to rising expectations for their potential to match the human v…