activity
20182021
most citedInvertible Denoising Network: A Light Solution for Real Noise Removal

11 citations · 21 across the 7 of their papers we have counts for

collaborators

14 papers

eess.IV202111 cited

Invertible Denoising Network: A Light Solution for Real Noise Removal

Yang Liu, Zhenyue Qin, Saeed Anwar +4

Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying inverti…

cs.HC2021

Observers Pupillary Responses in Recognising Real and Posed Smiles: A Preliminary Study

Ruiqi Chen, Atiqul Islam, Tom Gedeon +1

Pupillary responses (PR) change differently for different types of stimuli. This study aims to check whether observers PR can recognise real and posed smiles from a set of smile im…

cs.CR2020

Disguising Personal Identity Information in EEG Signals

Shiya Liu, Yue Yao, Chaoyue Xing +1

There is a need to protect the personal identity information in public EEG datasets. However, it is challenging to remove such information that has infinite classes (open set). We…

cs.CV2020

RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition

Yan Yang, Md Zakir Hossain, Tom Gedeon +1

Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways.…

cs.CV20204 cited

Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable

Yang Liu, Zhenyue Qin, Saeed Anwar +2

Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional perfo…

cs.CL20201 cited

A Token-wise CNN-based Method for Sentence Compression

Weiwei Hou, Hanna Suominen, Piotr Koniusz +2

Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fiel…