Hui Fang

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Senior Lecturer

Loughborough University

United Kingdom

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About

Hui Fang is a Senior Lecturer at Loughborough University, UK. His research interests encompass a wide range of topics including machine learning applications in medical imaging, backdoor attack defenses, and memory-augmented learning. Recent articles authored by Professor Fang focus on enhancing robustness in security frameworks, improving image super-resolution techniques, and advancing predictive modeling for various applications.

Recent Grants

Grant: Close

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19

Open Date: 2021-02-01

Close Date: 2022-01-31

Articles (20)

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Collaborators (5)

YINING HUA

Lecturer

University of Aberdeen

UNITED KINGDOM

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He Haitao

Lecturer / Assistant Professor

Loughborough University

UNITED KINGDOM

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Dewei Yi

University of Aberdeen

UNITED KINGDOM

View details

Jie Sui

University of Aberdeen

UNITED KINGDOM

View details

Jinya Su

University of Aberdeen

UNITED KINGDOM

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Hui Fang

Research Interests

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About

Recent Grants

Articles (20)

Enhancing robustness of backdoor attacks against backdoor defenses

Attack-Defending Contrastive Learning for Volumetric Medical Image Zero-Watermarking

A Memory-augmented Conditional Neural Process model for traffic prediction

Self-supervised memory learning for scene text image super-resolution

Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach

A novel attention model across heterogeneous features for stuttering event detection

Fast Aging-Aware Timing Analysis Framework With Temporal–Spatial Graph Neural Network

Modal adaptive super-resolution for medical images via continual learning

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

Stereoscopic image super-resolution with interactive memory learning

Domain‐adapted driving scene understanding with uncertainty‐aware and diversified generative adversarial networks

A multi-strategy contrastive learning framework for weakly supervised semantic segmentation

A Compositional Transformer Based Autoencoder for Image Style Transfer

Semantics‐guided generative diffusion model with a 3DMM model condition for face swapping

Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

Improving Synthetic to Realistic Semantic Segmentation With Parallel Generative Ensembles for Autonomous Urban Driving

GCT-UNET: U-Net Image Segmentation Model for a Small Sample of Adherent Bone Marrow Cells Based on a Gated Channel Transform Module

Hiding multiple images into a single image via joint compressive autoencoders

Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts

Multi-strategy hybrid heuristic algorithm for single container weakly heterogeneous loading problem

Collaborators (5)

YINING HUA

He Haitao

Dewei Yi

Jie Sui

Jinya Su

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