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20182024
most citedRecurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study

2 citations · 2 across the 1 of their papers we have counts for

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cs.CV2024

WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing

Seongmin Hwang, Daeyoung Han, Cheolkon Jung +1

The surge in interest regarding image dehazing has led to notable advancements in deep learning-based single image dehazing approaches, exhibiting impressive performance in recent…

cs.CV2020

Edge and Identity Preserving Network for Face Super-Resolution

Jonghyun Kim, Gen Li, Inyong Yun +2

Face super-resolution (SR) has become an indispensable function in security solutions such as video surveillance and identification system, but the distortion in facial components…

cs.CV2019

Adversarial Defense by Suppressing High-frequency Components

Zhendong Zhang, Cheolkon Jung, Xiaolong Liang

Recent works show that deep neural networks trained on image classification dataset bias towards textures. Those models are easily fooled by applying small high-frequency perturbat…

cs.CV2019

GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputs

Zhendong Zhang, Cheolkon Jung

Gradient boosted decision trees (GBDTs) are widely used in machine learning, and the output of current GBDT implementations is a single variable. When there are multiple outputs, G…

cs.CV20192 cited

Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study

Zhendong Zhang, Cheolkon Jung

Recurrent convolution (RC) shares the same convolutional kernels and unrolls them multiple steps, which is originally proposed to model time-space signals. We argue that RC can be…

cs.CV2018

PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Andrey Ignatov, Radu Timofte, Thang Van Vu +45

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…