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Convolutional neural networks (CNNs) apply well with food image recognitiondue to the ability to learn discriminative visual features. Nevertheless,recognizing distorted images is challenging for existing CNNs. Hence, the studymodelled a generalized specialist approach to train a quality resilientensemble. The approach aids the models in the ensemble framework retain generalskills of recognizing clean images and shallow skills of classifying noisyimages with one deep expertise area on a particular distortion. Subsequently, anovel data augmentation random quality mixup (RQMixUp) is combined withsnapshot ensembling to train G-Specialist. During each training cycle ofG-Specialist, a model is fine-tuned on the synthetic images generated byRQMixup, intermixing clean and distorted images of a particular distortion at arandomly chosen level. Resultantly, each snapshot in the ensemble gainedexpertise on several distortion levels, with shallow skills on other qualitydistortions. Next, the filter outputs from diverse experts were fused forhigher accuracy. The learning process has no additional cost due to a singletraining process to train experts, compatible with a wide range of supervisedCNNs for transfer learning. Finally, the experimental analysis on threereal-world food and a Malaysian food database showed significant improvementfor distorted images with competitive classification performance on pristinefood images.

Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble