Deep Learning Classifier

Deep learning classifiers are artificial neural networks designed to categorize data into predefined classes, aiming for high accuracy and robustness. Current research emphasizes improving classifier reliability through techniques like novel fault detection, exploring alternative loss functions (e.g., replacing cross-entropy), and enhancing explainability via methods such as Grad-CAM and its variants. These advancements are crucial for building trustworthy AI systems across diverse applications, from medical image analysis and spam detection to spectrum sharing in next-generation communication networks, where robust and interpretable classification is paramount.

Papers

January 17, 2022

Data-Centric Machine Learning in the Legal Domain
Hannes Westermann, Jaromir Savelka, Vern R. Walker, Kevin D. Ashley, Karim Benyekhlef
Machine Learning Model Data Centric Classification Performance Deep Learning Classifier

January 8, 2022

A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers
Shuyue Guan, Murray Loew
Medical Image Segmentation Based Approach Segmentation Model Segmentation Result Deep Learning Classifier Stealthy Attack CNN Classifier Location Heatmaps

December 1, 2021

Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis
Ali Bou Nassif, Abdollah Masoud Darya, Ashraf Elnagar
Deep Learning Model Pre Trained Long Short Term Memory Empirical Evaluation Deep Learning Classifier Shallow Learning Arabic Sentiment Analysis

November 25, 2021

A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for Deep Learning Classifier
Qiuyu Zhu, Xuewen Zu
Loss Function Latent Representation Latent Feature Deep Learning Classifier Softmax Loss

Deep Learning Classifier

Papers

Data-Centric Machine Learning in the Legal Domain

A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers

Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis

A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for Deep Learning Classifier