## 2 Literature Review  
[PERSON] et al. [13] first proposed a sparse representation classification (SRC) method for face recognition exploiting the discrimination capability of the dictionary. A competitive environment of effective dictionary learning problems evolved, and such problems became very challenging. Motivated by the sparse representation capability of a discriminative dictionary, various dictionary learning methods evolved, such as supervised dictionary learning [32], the kernel method [33], the proximal method [34], nonparametric Bayesian inference [25], and so on. Various techniques of learning dictionaries could be categorized as multiple dictionary learning [27], compact dictionary learning [35], and discriminative dictionary learning [8; 12; 36]. [PERSON] et al. [27] trained multiple class-specific dictionaries using visual correlation within the objects. An incoherence term was introduced to the loss function for inducing class-specific discrimination between various class-specific dictionaries [37]. However, this technique was computationally expensive. To address the large-scale datasets, compact dictionary learning techniques evolved. An entropy minimization rule was utilized to pick the dictionary atoms for the representation of examples based upon a probabilistic model [38]. Additionally, the kernel regularization method was also employed for nonlinear dictionary atoms training, which reduced the dimensions of features [33]. However, this method could not induce effective discrimination in the learned dictionaries. To induce discrimination in the dictionaries, authors followed approaches to get label information induced into the loss function at the training stage. Logistic loss [39], quadratic loss [8], and hinge loss [40] are some of the examples of commonly used loss functions. Using label along with feature data [23], a discriminative method, K-SVD (D-KSVD), was proposed to learn the dictionary and linear classifier simultaneously. [PERSON] et al. [8; 41] enhanced the dictionary's discrimination ability by introducing a label-consistent constraint. [PERSON] et al. [12] proposed a structured dictionary scheme via Fisher discrimination criteria, utilizing both the reconstruction errors and the sparse codes for classification. The recognition accuracy was increased by developing a method based on low-rank representation to eliminate the correlation between different categories [42]. These optimization-based dictionary learning algorithms could not adaptively learn sparsity level and noise variance to produce the best results. To overcome these limitations, dictionary learning problems were handled through Bayesian techniques. A nonparametric beta-Bernoulli process model was developed by [43] to learn dictionary atoms and sparse codes for image denoising and interpolation, which is capable of inferring the number of dictionary atoms from training data. [PERSON] et al. [24; 25], [PERSON] and [PERSON] [44] presented a joint discriminative dictionary and classifier learning algorithm for image classification using Gibbs sampling. A variational inference method was developed by [45] to train a dictionary for image denoising. Deep learning methods were also gaining popularity in the computer vision field [46]. Convolutional neural networks (CNNs) were taking a lead [47]. For instance, a deep residual network [48] with a network depth of 100 was proposed for image classification. [PERSON] et al. [49] proposed a densely connected CNN to strengthen feature propagation, increasing the classification accuracy. The multiple-layer architecture was the success of deep learning methods for superior approximation capacity. The total number of parameters involved in a deep model is very large. For the optimization of massive parameters, a large volume of data are required during training along with computational power. Therefore, for medium-scale datasets, dictionary-learning-based methods are suitable compared with deep learning methods.