All Outputs (3)

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery (2022)
Presentation / Conference Contribution
Bhowmik, N., Barker, J., Gaus, Y., & Breckon, T. (2022, June). Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery. Presented at 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, Louisiana

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression c... Read More about Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery.

PANDA: Perceptually Aware Neural Detection of Anomalies (2021)
Presentation / Conference Contribution
Barker, J., & Breckon, T. (2021, July). PANDA: Perceptually Aware Neural Detection of Anomalies. Presented at 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China

Semi-supervised methods of anomaly detection have seen substantial advancement in recent years. Of particular interest are applications of such methods to diverse, real-world anomaly detection problems where anomalous variations can vary from the vis... Read More about PANDA: Perceptually Aware Neural Detection of Anomalies.