## 3 Methodology
### Visual Features via Quality Resilient Convolutional Neural Network  
Visual features extracted from the food photos with various quality distortions via a quality resilient convolutional neural network (CNN) are nearly as discriminatory as the features from the pristine images. In an ideal case, the visual feature vectors from the pristine and distorted food photos must be equally discriminant with similar classification accuracy. Previous research by [PERSON] et al. [1] found that for food category datasets, features extracted from the fine-tuned InceptionResNetV2 model have superior classification performance as compared to ResNet-50 and DenseNet-201 model. Based on their findings, we employed the InceptionResNetV2 model pre-trained on the ImageNet dataset and fine-tuned it on food datasets for feature extraction. However, in real-world settings,  
Figure 1: Flow chart of the proposed framework. The red dot presents new neurons during progressive learning. the poor quality of food photos affects the robustness of the model employed for feature extraction. An evaluation is provided of the InceptionResNetV2 to study the impact on the model under five common types of image distortions: (1) Gaussian blur; (2) Gaussian noise; (3) Defocus blur; (4) Contrast; (5) JPEG Compression [27] that occurs in the real-world situation at different parameter settings. Then, to improve the robustness of the model for the most expected distortions in practical applications, we fine-tuned the network employed for feature extraction on food images using our novel data augmentation strategy RIMixUp. The idea behind our data augmentation method is to generate a single synthetic image that carries the knowledge of clean and noisy images. Based on this idea, proposed data augmentation advances the mixup theory to generate a single synthetic image for tackling quality distortions. We exploited mixup theory, as theoretical analysis by [PERSON] et al. [28] shows that mixup regularizes the neural network to favor simple linear behavior in-between training examples. As a result, it improves generalization and robustness. Figure 2 gives a visual illustration of our process. In step1, it generates a single image by iteratively mixing randomly selected images distorted with Gaussian noise, defocus blur, and Gaussian blur using the mixup strategy [29]. The method generates a new synthetic image by mixing the pristine image with the synthetic image generated in step 1, using the mixup strategy. Equations (1) and (2) generates the synthetic images. \[x_{f}=\lambda x_{i}+(1-\lambda)x_{n} \tag{1}\]  
\[y_{f}=\lambda y_{i}+(1-\lambda)y_{n}, \tag{2}\]  
where \(x_{i}\) are the raw input vectors of the clean images and \(x_{n}\) are the random iterative mixup of raw input vectors of the noisy images. Similarly, \(y_{i}\) are the one-hot label encodings of clean images, and \(y_{n}\) are the random iterative mixup of one hot label encodings. Here \(\lambda\) are values with [0, 1] range and are sampled from the beta distribution. (\(x_{f}\), \(y_{f}\)) refers to the final synthetic training samples. Synthetic images generated using RIMixup data augmentation for the food datasets fine-tunes the InceptionResnetV2 model pre-loaded with weights trained on ImageNet. Figure 3 shows the process of training a quality resilient convolutional neural network (CNN) to extract visual features. Our approach makes the model used for feature extraction more invariant to image quality. It has several benefits in contrast to state-of-the-art methods for the most expected distortions levels. Unlike DeepCorrect [30], it does not require any change in the architecture of the model. It does not increase training time like fine-tuning with low-quality images, as the size of the training data remains the same. Nor does it increase the number of FLOPS for the test image. Unlike stability, training [31] and feature quantization methods [32], it does not affect the top 1 accuracy of the pristine images. In the experimental section of this work, we have briefly discussed our findings.