Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
This paper presents a loss-aware feature-map pruning framework using multi-armed bandits. Each feature map is treated as an arm; temporary masking and loss change evaluation guide removal decisions. Results on multiple datasets show that UCB1 and Thompson Sampling preserve accuracy close to unpruned models while reducing computation, outperforming greedy and magnitude-based pruning.
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[Submitted on 29 May 2026]
Title:Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
View a PDF of the paper titled Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits, by Salem Ameen and 1 other authors
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Abstract:Convolutional neural networks often contain redundant feature maps that increase storage and inference cost. This paper presents a loss-aware feature-map pruning framework using multi-armed bandits. Feature-map pruning is structured because it removes complete convolutional output channels and their producing filters rather than isolated scalar weights. Each candidate feature map is treated as an arm. At each play time, one map is temporarily masked and evaluated on a sampled mini-batch; the map is then restored and the observed loss change is converted into a safe-removal reward. After a fixed play budget, candidate maps are ranked by learned scores and the top-k maps are permanently removed with their filters, biases and corresponding next-layer input-channel kernels. The study evaluates UCB1 and Thompson Sampling, compares them with direct/oracle-style evaluation on LeNet/MNIST, and extends the evaluation to MNIST, CIFAR-10, CIFAR-100, SVHN, CUB-200-2011 and Oxford Flowers 102. Results show that UCB1 and Thompson Sampling preserve accuracy close to unpruned models while removing feature maps and reducing convolutional computation. Friedman and Nemenyi tests show that UCB1 obtains the highest mean rank, followed by Thompson Sampling; both significantly outperform greedy and magnitude-based pruning while remaining statistically comparable to the original unpruned model.
Comments: 18 pages, 4 figures
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22564 [cs.AI]
(or arXiv:2607.22564v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22564
arXiv-issued DOI via DataCite
Submission history
From: Salem Ameen [view email] [v1] Fri, 29 May 2026 20:03:08 UTC (560 KB)
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