Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout
Convolutional Neural Networks(CNNs);Probabilistic Feature Importance Dropout (PFID);Enhanced Dropout Optimization;Regularization Techniques;Adaptive Learning;Network Efficiency.
Format violating ICLR 2025 guidelines.
Submission Desk Rejected by Program Chairs
@misc{
ghayoumi2024enhancing,
title={Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout},
author={Mehdi Ghayoumi},
year={2024},
url={https://openreview.net/forum?id=i0TOAkzGF8}
}
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.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
~Mehdi_Ghayoumi1
Mehdi Ghayoumi
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Convolutional Neural Networks(CNNs) Probabilistic Feature Importance Dropout (PFID) Enhanced Dropout Optimization Regularization Techniques Adaptive Learning Network Efficiency.
I certify that there is no acknowledgement section in this submission for double blind review.
ghayoumi|enhancing_efficiency_and_regularization_in_convolutional_neural_networks_strategies_for_optimized_dropout
/pdf/60575abfe7140f0db11b98de1b9eaf02e233504c.pdf
unsupervised, self-supervised, semi-supervised, and supervised representation learning
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
/attachment/9dc35157978d4a47d3ecc89d4f6099b00ffe893b.pdf
Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission