All Outputs (3)

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Conference Proceeding
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. . https://doi.org/10.5220/0011684700003417

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)
Conference Proceeding
Bhowmik, N., Barker, J., Gaus, Y., & Breckon, T. (2022). Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery. . https://doi.org/10.1109/cvprw56347.2022.00052

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)
Conference Proceeding
Barker, J., & Breckon, T. (2021). PANDA: Perceptually Aware Neural Detection of Anomalies. . https://doi.org/10.1109/ijcnn52387.2021.9534399

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.