Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout

Authors: Mehdi Ghayoumi
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Convolutional Neural Networks(CNNs), Probabilistic Feature Importance Dropout (PFID), Enhanced Dropout Optimization, Regularization Techniques, Adaptive Learning, Network Efficiency.

Abstract:
This study explores dropout optimization in Convolutional Neural Networks (CNNs), aiming to beat traditional approaches in regularization and efficiency. We introduce dynamic, context-aware strategies, embodied by Probabilistic Feature Importance Dropout (PFID). This method modifies dropout rates to the unique learning phase of CNNs, integrating adaptive, structured, and contextual dropout techniques. Experimentation, benchmarked against current state-of-the-art methods, demonstrates improvements in network performance, particularly in generalization and training efficiency. We also discuss our findings. The findings represent an advancement in dropout techniques, offering more adaptable and robust CNN models for complex datasets and computational landscapes.

Decision: desk reject

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