Toby Breckon

Professor

University of Durham

United Kingdom

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About

Professor Toby Breckon is a faculty member at Durham University in the United Kingdom. His research interests encompass various aspects of machine learning, including neural architecture search, image classification, and face recognition bias. He has published recent articles that explore topics such as domain adaptation, data augmentation, and deep learning applications in security imaging and autonomous systems.

Articles (13)

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2026

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2025

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2020

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2020

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2020

Collaborators (3)

Khalid Ismail

Birmingham City University

UNITED KINGDOM

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Qian Wang

Durham University

UNITED KINGDOM

View details

Fanlin Meng

The University of Manchester

UNITED KINGDOM

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Toby Breckon

Research Interests

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Articles (13)

DDS-NAS: Dynamic data selection within neural architecture search via on-line hard example mining applied to image classification

Racial Bias within Face Recognition: A Survey

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

Data augmentation with norm-AE and selective Pseudo-Labelling for Unsupervised Domain Adaptation

Generalized zero-shot domain adaptation via coupled conditional variational autoencoders

Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss

Temporal and non-temporal contextual saliency analysis for generalized wide-area search within unmanned aerial vehicle (UAV) video

Cross-domain structure preserving projection for heterogeneous domain adaptation

Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging

Learning to Drive: End-to-End Off-Road Path Prediction

A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes

Adopting Deep Learning Methods for Airborne RGB Fluvial Scene Classification

An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening

Collaborators (3)

Khalid Ismail

Qian Wang

Fanlin Meng

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