114 citations · 579 across the 79 of their papers we have counts for
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Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
Jack W Barker, Neelanjan Bhowmik, Yona Falinie A Gaus +1
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that…
Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
Qian Wang, Toby P. Breckon
Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e.g., texts and images) or…
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Qian Wang, Toby P. Breckon
Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain…
Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation
Naif Alshammari, Samet Akçay, Toby P. Breckon
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) perform…
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
Fady Medhat, Mahnaz Mohammadi, Sardar Jaf +6
Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be pres…