# Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble

arXiv: https://arxiv.org/abs/2206.05853 · HTML: https://mlsanity.com/paper/2206.05853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 12 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Improving the Robustness of Deep Neural Networks via Stability Training](https://mlsanity.com/paper/1604.04326/) (2016)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [How Image Degradations Affect Deep CNN-based Face Recognition?](https://mlsanity.com/paper/1608.05246/) (2016)
- [DeepCorrect: Correcting DNN models against Image Distortions](https://mlsanity.com/paper/1705.02406/) (2017)
- [FoodNet: Recognizing Foods Using Ensemble of Deep Networks](https://mlsanity.com/paper/1709.09429/) (2017)
- [Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches](https://mlsanity.com/paper/1610.06756/) (2016)
- [Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data](https://mlsanity.com/paper/1812.10179/) (2018)

## Abstract (arXiv)

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.