15 citations · 23 across the 7 of their papers we have counts for
7 papers
Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention
Zhentao Fan, Hongming Chen, Yufeng Li
Recently, Transformer-based architecture has been introduced into single image deraining task due to its advantage in modeling non-local information. However, existing approaches t…
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Xiang Chen, Zhentao Fan, Pengpeng Li +5
We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. This paper provides a new perspective to treat image dehazing as…
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Xiang Chen, Jinshan Pan, Kui Jiang +5
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible. Ho…
Addressing the confounds of accompaniments in singer identification
Tsung-Han Hsieh, Kai-Hsiang Cheng, Zhe-Cheng Fan +2
Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors fro…
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products
Zhe-Cheng Fan, Tak-Shing T. Chan, Yi-Hsuan Yang +1
Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of s…
SVSGAN: Singing Voice Separation via Generative Adversarial Network
Zhe-Cheng Fan, Yen-Lin Lai, Jyh-Shing Roger Jang
Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In th…
Music Signal Processing Using Vector Product Neural Networks
Z. C. Fan, T. S. Chan, Y. H. Yang +1
We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real val…