18 citations · 28 across the 3 of their papers we have counts for
6 papers
Multi-Attention Based Ultra Lightweight Image Super-Resolution
Abdul Muqeet, Jiwon Hwang, Subin Yang +3
Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performa…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
Towards an Adversarially Robust Normalization Approach
Muhammad Awais, Fahad Shamshad, Sung-Ho Bae
Batch Normalization (BatchNorm) is effective for improving the performance and accelerating the training of deep neural networks. However, it has also shown to be a cause of advers…
Hybrid Residual Attention Network for Single Image Super Resolution
Abdul Muqeet, Md Tauhid Bin Iqbal, Sung-Ho Bae
The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN feature…
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis
Kang-Ho Lee, JoonHyun Jeong, Sung-Ho Bae
Due to a resource-constrained environment, network compression has become an important part of deep neural networks research. In this paper, we propose a new compression method, \t…
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms
Joonhyun Jeong, Sung-Ho Bae
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing bu…