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.

该研究使用卷积神经网络结合数据增强和快照集成的广义专家方法来解决图像失真问题，从而提高了食品图像识别的准确性和稳健性。

建模广义专家方法以训练质量弹性快照集合

Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble

Ensembles of neural networks are known to be much more robust and accurate
than individual networks. However, training multiple deep networks for model
averaging is computationally expensive. In this paper, we propose a method to
obtain the seemingly contradictory goal of ensembling multiple neural networks
at no additional training cost. We achieve this goal by training a single
neural network, converging to several local minima along its optimization path
and saving the model parameters. To obtain repeated rapid convergence, we
leverage recent work on cyclic learning rate schedules. The resulting
technique, which we refer to as Snapshot Ensembling, is simple, yet
surprisingly effective. We show in a series of experiments that our approach is
compatible with diverse network architectures and learning tasks. It
consistently yields lower error rates than state-of-the-art single models at no
additional training cost, and compares favorably with traditional network
ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain
error rates of 3.4% and 17.4% respectively.

本文提出了一种名为 ' 快照集成 ' 的新技术，可以实现在不增加训练成本的情况下，通过在一个单一的神经网络中收敛到几个局部极小值并保存模型参数的方法，同时通过循环学习率表达式，获得重复快速收敛的能力，实验结果表明，这种方法可有效地降低误差率并与传统的网络集成方法相比较优秀。