Capacity-Aware Gradient-Boosted CNNs and DNNs for Efficient Image Classification

Jujjuvarapu Nagendra; Krishna Kumar Singh

Capacity-Aware Gradient-Boosted CNNs and DNNs for Efficient Image Classification

Author(s)	Mr. Jujjuvarapu Nagendra, Mr. Krishna Kumar Singh
Country	India
Abstract	Gradient-boosted convolutional neural networks (GB-CNNs) grow a classifier by sequentially adding and freezing dense layers fitted to pseudo-residuals, yet the parameter cost of this additive head is seldom examined. This article proposes a capacity-aware GB-CNN (CW-GB-CNN) built on a closed-form capacity model of the additive dense head, Phead(W, M), which gives the head's parameter count from the layer width W and the number of boosting rounds M. The width of the boosted dense layers is selected by weighing the modeled parameter cost against the accuracy it returns, rather than being fixed a priori. CW-GB-CNN is compared with jointly trained networks, a gradient-boosted deep network, and classical tree ensembles on five public image datasets using rank-based statistical tests. Boosting reduced the test error of the jointly trained CNN by 49.7% on average, and CW-GB-CNN achieved the best mean accuracy (95.66%) and mean rank (1.6). On CIFAR-10, its 20-neuron head matched a conventional 128-neuron head (87.77% versus 87.86%) with 6.66 times fewer head parameters and 1.70 times fewer total parameters.
Keywords	Convolutional Neural Networks, Deep Neural Networks, Gradient Boosting, GB-CNN, Capacity-Aware GB-CNN (CW-GB-CNN), GB-DNN, Pseudo-Residual Learning, Dense-Layer Width, Parameter Efficiency, Ensemble Learning, Statistical Model Comparison, Image Classification
Field	Engineering
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-26

View / Download PDF File

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Capacity-Aware Gradient-Boosted CNNs and DNNs for Efficient Image Classification

Share this