Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble

TL;DR

This paper introduces a generalized specialist ensemble approach with RQMixUp augmentation to improve CNN robustness against distorted images, achieving higher accuracy in food image recognition.

Contribution

The study proposes a novel generalized specialist ensemble method combined with RQMixUp augmentation for training resilient CNNs against image distortions.

Findings

Significant accuracy improvement on distorted food images.

Competitive performance on clean images.

Effective fusion of diverse expert outputs.

Abstract

Convolutional neural networks (CNNs) apply well with food image recognition due to the ability to learn discriminative visual features. Nevertheless, recognizing distorted images is challenging for existing CNNs. Hence, the study modelled a generalized specialist approach to train a quality resilient ensemble. The approach aids the models in the ensemble framework retain general skills of recognizing clean images and shallow skills of classifying noisy images with one deep expertise area on a particular distortion. Subsequently, a novel data augmentation random quality mixup (RQMixUp) is combined with snapshot ensembling to train G-Specialist. During each training cycle of G-Specialist, a model is fine-tuned on the synthetic images generated by RQMixup, intermixing clean and distorted images of a particular distortion at a randomly chosen level. Resultantly, each snapshot in the ensemble gained expertise on several distortion levels, with shallow skills on other quality distortions. Next, the filter outputs from diverse experts were fused for higher accuracy. The learning process has no additional cost due to a single training process to train experts, compatible with a wide range of supervised CNNs for transfer learning. Finally, the experimental analysis on three real-world food and a Malaysian food database showed significant improvement for distorted images with competitive classification performance on pristine food images.