125 citations · 218 across the 6 of their papers we have counts for
6 papers
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
HyunJae Lee, Hyo-Eun Kim, Hyeonseob Nam
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective. In this paper,…
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Hyo-Eun Kim, Seungwook Kim, Jaehwan Lee
Data is one of the most important factors in machine learning. However, even if we have high-quality data, there is a situation in which access to the data is restricted. For examp…
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Hyeonseob Nam, Hyo-Eun Kim
Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations…
Semantic Noise Modeling for Better Representation Learning
Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, ge…
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
Hyo-Eun Kim, Sangheum Hwang
A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and dec…
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Sangheum Hwang, Hyo-Eun Kim
Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural ne…