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"Knowledge Distillation and Continual Learning for Optimized Deep Neura" by Vu Minh Hieu Phan

Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations.
Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally ....

Deep Learning , Knowledge Distillation , Continual Learning , Semantic Segmentation , Ignal Classification ,

"Classification of Improvised Explosive Devices using Multilevel Projec" by Fok Hing Chi Tivive, Abdesselam Bouzerdoum et al.

Improvised explosive devices pose a significant threat to defence forces and humanitarian demining personnel. They are weapons of modern times, made from non-conventional military materials, rendering them difficult to identify when buried in the ground. Numerous studies focus on detecting these explosive threats and reducing the false alarm rate. However, there are few attempts to identify the detected explosive devices to take proper countermeasures. This paper presents a multi-level projective dictionary learning method to classify ground penetrating radar signals from improvised explosive devices. The proposed dictionary learning method solves three different tasks simultaneously: suppressing background clutter, learning a set of discriminative features for classification, and training a classifier. The suppression of ground clutter is formulated as a low-rank optimization problem with sparse constraints, where a low-rank subspace is learnt from background clutter signals. Dictiona ....

Lutter Removal Using Low Rank Constraint , Dictionary Learning , Feature Extraction , Ground Penetrating Radar , Improvised Explosive Devices , Landmine Detection , Machine Learning , Ignal Classification ,