## 2 Related Work  
We examine the existing methodologies that have been developed so far for automatic food recognition. It includes approaches ranging from distance-based methods, a probabilistic model and decision tree, support vector machine, extreme learning machine, convolution neural networks to ensembles. [PERSON] et al. studied the probabilistic model and decision tree for the food, restaurant, and dish images dataset [3]. Several approaches use distance-based methodology for food image classification [4; 5]. These classifiers used handcrafted features such as color and wavelet texture properties, Gabor, Shape, scale-invariant feature transform (SIFT), and Bag of Features (BoF) for learning. The handcrafted features from these methods are not robust to image quality. Moreover, these classifiers do not incrementally learn new food concepts and ingredients. Support vector machine is widely applied to classify features extracted from different methods. [PERSON] et al. [6] used a fisher vector for encoding histogram of oriented gradients (HOG) and color patches. Some variants of support vector machine (SVM) uses features from a bag of words, color histogram, and the D-SIFT approach [7]. The researchers also explored local, including global and color, features [8], SIFT and Shape context descriptors [9], and pairwise local features [10] for training SVM. Several SVM methods use the edge detection method [11], counting the pixels in the region of interest, SIFT-BoF, Color-Sift with the spatial pyramid [12], Color hue, and multiple types of SIFT features [13]. Some research work focuses on the hybrid aggregation of sparse, color, and edge histogram [14] for feature extraction and SVM for classification. The multi-view multi-kernel SVM [15] detects both ingredients and categories, but it has high computational complexity, and it cannot learn novel concepts. Several food logging applications integrate the SVM-based frameworks [6,16]. However, most of these studies focused on the feature extraction part. They have used fixed-class architecture for classification, which does not progressively learn new food classes and ingredients. Moreover, they have not employed explainable artificial intelligence (AI) methods to understand the contributions of an area of interest in the food photos towards model predictions. Convolutional neural networks have achieved state-of-the-art performances on many food recognition datasets [17, 18, 19, 20, 21]. Recent studies have explored a variety of CNN-based methods for their datasets. These methods range from the asymmetric multi-task convolutional neural network [22] and the spatial pyramid convolutional neural network [22] to the multi-scale multi-view deep network [23]. However, none of these methods studied the impact of quality distortion in the images on the robustness of features. These studies used a linear-fully connected classifier of CNN for classification, which has a fixed structure. It cannot progressively learn new food classes and ingredients. It is noteworthy that most studies classify only food categories. [PERSON] et al. [24] proposed Arch-D architecture for detecting food categories and ingredients. However, their method uses batch learning and, as a result, it cannot adapt domain changes within the existing concepts or learn new classes. Sequential personalized classifier (SPC) [25] is a personalized framework for food recognition that only classifies food categorizes. During online learning, SPC is unable to detect data instances with noisy labels. SPC also does not assign labels to an unlabeled dataset. Moreover, it does not interpret the contribution of the pixels in food photos towards classification. Recently, researchers scrutinized extreme learning machines for food classification. Supervised Extreme Learning Committee [26], uses committee members, \"kernel extreme learning machine\" trained to specialize single feature type. It has high complexity as it uses all the features of training images for computing kernel matrix. [PERSON] et al.