# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 2 Related Work
The SGLDM outperformed state-of-the-art methods and could synthesize high-quality face images with different expressions, facial accessories, and hairstyles. Earlier, [PERSON] et al. [52] provided more information about digital image denoising based on a comprehensive overview of the backpropagation algorithm. However, these recent studies well demonstrated significant progress in the development of effective and efficient methods for handling face sketch recognition. In the literature, the SMA has been applied to solve a wide variety of real-world problems, such as economic emission dispatch [53], image segmentation [54], and the estimation of solar photovoltaic cell parameters [55]. Researchers proposed several variations of the SMA to further enhance its performance in specific domains. [PERSON] et al. [36] proposed a unique NSD-based multilevel thresholding strategy that employed the Leader Slime Mold Algorithm (LSMA). The LSMA outperformed the traditional SMA by using the three best candidates as a leader to guide the search. The study also applied the NSD-LSMA method for multilevel thresholding of Landsat images and showed that it outperformed state-of-the-art methods. In another study, [PERSON] et al. [37] introduced an Adaptive Opposition Slime Mold Algorithm (AOSMA) for function optimization that adaptively decided whether to use opposition-based learning to enhance exploration and replaced one random search agent with the best one to maximize exploitation. The AOSMA outperformed other state-of-the-art optimization algorithms in qualitative and quantitative analyses. The algorithm was suggested to be useful for function optimization to solve real-world engineering problems. Concurrently, another study by [PERSON] et al. [56] showed the development of a new Equilibrium Slime Mold Algorithm (ESMA) for multilevel thresholding of breast thermogram images based on minimizing the entropic dependencies among different classes in the image. The ESMA was evaluated and compared to other optimization algorithms and found to perform better. The study claimed that the proposed method might be useful for assisting medical practitioners in breast thermogram analysis. [PERSON] et al. [57] proposed HAOASMA, a hybrid algorithm that combined AOA and SMA to address the limitations of SMA's exploration and exploitation capabilities. They integrated lens opposition-based learning to increase population diversity and accelerate convergence. HAOASMA outperformed traditional SMA algorithms and is better for finding optimal global solutions. Recently, [PERSON] [58] proposed the Chaotic SMA (CSMA) to improve the SMA method by applying ten different chaotic maps to generate chaotic values instead of random values. It was intended to speed up SMA's global convergence and keep it from becoming trapped in its local solutions using chaotic maps. CSMA performed better in 62 benchmark functionsand real-world engineering design challenges than other methods and standard SMA. Although very few studies have attempted to use different optimization techniques to solve the face sketch recognition problem, none have used SMA and its variants. Therefore, the current study aims to conduct further investigation on the effectiveness of the optimization algorithms to solve the face sketch recognition problem and to develop a new algorithm to deal with large-scale problems, including the face sketch recognition problem. In addition, the use of multiple pre-trained deep learning models and their simultaneous fine-tuning using S\({}^{2}\)SMA can be considered a novel approach to improve the accuracy of face sketch recognition.