115 citations · 205 across the 11 of their papers we have counts for
8 papers · 1 filter
Temporal Dynamic Quantization for Diffusion Models
Junhyuk So, Jungwon Lee, Daehyun Ahn +2
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility. However, high storage and computation demands, result…
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking
Ilchae Jung, Minji Kim, Eunhyeok Park +1
This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural netwo…
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold
Changhun Lee, Hyungjun Kim, Eunhyeok Park +1
Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degrada…
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation
Hyunyoung Jung, Eunhyeok Park, Sungjoo Yoo
Self-supervised monocular depth estimation has been widely studied, owing to its practical importance and recent promising improvements. However, most works suffer from limited sup…
PROFIT: A Novel Training Method for sub-4-bit MobileNet Models
Eunhyeok Park, Sungjoo Yoo
4-bit and lower precision mobile models are required due to the ever-increasing demand for better energy efficiency in mobile devices. In this work, we report that the activation i…
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
Hyunsu Kim, Ho Young Jhoo, Eunhyeok Park +1
Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and c…
Precision Highway for Ultra Low-Precision Quantization
Eunhyeok Park, Dongyoung Kim, Sungjoo Yoo +1
Neural network quantization has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To re…
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo +3
Although the latest high-end smartphone has powerful CPU and GPU, running deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet classification on mobile de…