Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Omathil, Gelesh G.; Sreeja, C.S. Title A name given to the resource Co Adaptation Vs Signal Altering Regularization Layer in Deep Learning: A Trade Off Analysis via Node Redundancy and Transfer Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived INDISCON 2025 - IEEE 6th India Council International Subsections Conference, Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/INDISCON66021.2025.11252043" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INDISCON66021.2025.11252043</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030165687?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030165687?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Omathil G.G., Christ University, Department of Computer Science, Bangalore, India; Sreeja C.S., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource This study investigates the trade-off between incorporating regularization layers-such as Dropout, R-Drop, and Gaussian Dropout-and the phenomenon of co-adaptation in deep learning models. While regularization is designed to enhance generalization by disrupting hidden layer activations and reducing overfitting, it may also introduce node redundancy, potentially diminishing the model's capacity to learn efficiently. Conversely, co-adaptation, though often considered undesirable, may help preserve beneficial internal representations that contribute to learning generalizable data patterns-particularly in transfer learning scenarios-where regularization may inadvertently hinder such learning. Using the CIFAR-10 dataset, this study conducts an empirical analysis of how various regularization strategies influence neuron redundancy and downstream transfer performance. The results indicate that, although regularization effectively controls overfitting, excessive distortion in hidden representations can impair the model's ability to generalize across tasks. These findings provide insights into the need for balanced regularization strategies that maintain useful structure while minimizing detrimental redundancy. 2025 IEEE. Subject The topic of the resource Deep learning; Gaussian Dropout; Node redundancy; Overfitting; RDrop; Regularization; Regularization Layer; Transfer learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151504-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online