Efficient Adaptive Ensembling for Image Classification

• URL: http://arxiv.org/abs/2206.07394v3
• Date: Wed, 30 Aug 2023 06:36:08 GMT
• Title: Efficient Adaptive Ensembling for Image Classification
• Authors: Antonio Bruno, Davide Moroni, Massimo Martinelli
• Abstract summary: We propose a novel method to boost image classification performances without increasing complexity. We trained two EfficientNet-b0 end-to-end models on disjoint subsets of data. We were able to outperform the state-of-the-art by an average of 0.5$%$ on the accuracy.
• Score: 3.7241274058257092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent times, with the exception of sporadic cases, the trend in Computer Vision is to achieve minor improvements compared to considerable increases in complexity. To reverse this trend, we propose a novel method to boost image classification performances without increasing complexity. To this end, we revisited ensembling, a powerful approach, often not used properly due to its more complex nature and the training time, so as to make it feasible through a specific design choice. First, we trained two EfficientNet-b0 end-to-end models (known to be the architecture with the best overall accuracy/complexity trade-off for image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5$\%$ on the accuracy, with restrained complexity both in terms of the number of parameters (by 5-60 times), and the FLoating point Operations Per Second (FLOPS) by 10-100 times on several major benchmark datasets.

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