All Outputs (22)

Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation (2020)
Journal Article
Ramirez Rivera, A., Khan, A., Bekkouch, I. E. I., & Sheikh, T. S. (2022). Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 281-291. https://doi.org/10.1109/TNNLS.2020.3027667

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality o... Read More about Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation.

Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification (2020)
Journal Article
Ahmad, M., Mazzara, M., Raza, R. A., Distefano, S., Asif, M., Sarfraz, M. S., Khan, A. M., & Sohaib, A. (2020). Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification. Applied Sciences, 10(14), Article 4739. https://doi.org/10.3390/app10144739

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried ou... Read More about Multiclass non-randomized spectral-spatial active learning for hyperspectral image classification.