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Outputs (32)

Code Gradients: Towards Automated Traceability of LLM-Generated Code (2024)
Conference Proceeding
North, M., Atapour-Abarghouei, A., & Bencomo, N. (in press). Code Gradients: Towards Automated Traceability of LLM-Generated Code.

Large language models (LLMs) have recently seen huge growth in capability and usage. Within software engineering, LLMs are increasingly being used by developers to generate code. Code generated by an LLM can be seen essentially a continuous mapping f... Read More about Code Gradients: Towards Automated Traceability of LLM-Generated Code.

Insights from the Use of Previously Unseen Neural Architecture Search Datasets (2024)
Conference Proceeding
Geada, R., Towers, D., Forshaw, M., Atapour-Abarghouei, A., & Mcgough, A. S. (in press). Insights from the Use of Previously Unseen Neural Architecture Search Datasets.

The boundless possibility of neural networks which can be used to solve a problem-each with different performance leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removin... Read More about Insights from the Use of Previously Unseen Neural Architecture Search Datasets.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Conference Proceeding
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00301

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

Predicting the Performance of a Computing System with Deep Networks (2023)
Conference Proceeding
Cengiz, M., Forshaw, M., Atapour-Abarghouei, A., & McGough, A. S. (2023). Predicting the Performance of a Computing System with Deep Networks. In ICPE '23: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (91-98). https://doi.org/10.1145/3578244.3583731

Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performanc... Read More about Predicting the Performance of a Computing System with Deep Networks.

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification (2022)
Conference Proceeding
Bevan, P., & Atapour-Abarghouei, A. (2022). Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of Machine Learning Research (1874-1892)

Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma and other skin lesions, but prediction irregularities due to biases seen within the training data are an issue that should be addressed... Read More about Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification.

A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip (2022)
Conference Proceeding
Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. . https://doi.org/10.1109/bhi56158.2022.9926917

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in... Read More about A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip.

Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification (2022)
Conference Proceeding
Bevan, P. J., & Atapour-Abarghouei, A. (2022). Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification. In K. Kamnitsas, L. Koch, M. Islam, Z. Xu, J. Cardoso, Q. Doi, …S. Tsaftaris (Eds.), DART 2022: Domain Adaptation and Representation Transfer (1-11). https://doi.org/10.1007/978-3-031-16852-9_1

Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In th... Read More about Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification.