# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
### Related Work  
Due to factors like lighting, posture, and image quality, even identical models can exhibit significant performance disparities in similar tasks. Domain adaptation, a facet of transfer learning, seeks to mitigate the discrepancies between two distributions. Deep visual domain adaptation methods have historically been categorized into two groups, which are one-step DA and multi-step DA [15]. One-step DA can be further classified into three subcategories [16]. Discrepancy-based methods involve fine-tuning to reduce domain migration [17; 18; 19; 20]. Adversarial-based approaches employ discriminators to foster confusion through adversarial objectives [21; 22; 23; 24]. Instruction-based techniques utilize data reconstruction, which is used as an auxiliary task to guarantee feature consistency [25; 26; 27; 28]. Many projects are based on the idea that the disparity between two domains can be reduced by improving deep network models with labeled or unlabeled target data. These works focus on employing several strategies to align the statistical distribution shift between the source and target domains. maximum mean discrepancy (MMD), correlation alignment (CORAL), Kullback-Leibler divergence (KLD), H divergence, and other methods are frequently used to measure and lessen distribution shifts. Another class of domain adaptation techniques that has lately gained interest is adversarial learning. This method uses a domain discriminator to determine if a data point comes from the source domain or the destination domain through an adversarial objective, which is intended to promote domain confusion while decreasing the discrepancy between the source domain and target domain mapping distributions. An example shows how to create simulated samples similar to the target sample while retaining source domain annotation data by using source images, noise vectors, or a combination of both. Alternatively, the feature extractor uses labels from the source domain to learn a discriminating representation. Using a domain-confusion loss, this representation is then utilized to translate target domain data into the unified space, producing domain-invariant representations. The aforementioned techniques fall under the one-step DA subcategory. Similar to the single-step DA, multi-step DA has three subcategories [29]. With hand-crafted approaches, specialists identify intermediate domains based on their knowledge [30]. Instance-based techniques comprise intermediate domains by picking specific data segments from additional datasets, hence training the deep network [31]. By freezing previous trained networks and using their intermediate expressed as inputs for next networks, representation-based approaches promote transfer [32]. The multi-step DA technique starts from the intermediate domain where the correlation between the localization and the source domain and the target domain is stronger than their direct relationship. One-step DA is then used to transfer knowledge between the source, intermediate, and destination domains, minimizing information loss. It is important for multi-step DA to select the intermediate domain and use it efficiently. The common approach is to extract central data portions from auxiliary datasets to form meta-structure domains, subsequently training the deep learning network. The selective learning algorithm (SLA) serves as a notable technique, gradually choosing relevant unlabeled data from intermediate domains. This approach functions as a bridge to bridge the substantial distribution gap, enabling knowledge transfer between distant domains. SLA is adeptly employed to address the distant domain transfer learning (DDTL) challenge. It leverages supervised autoencoders or supervised convolutional autoencoders as base models, accommodating various input types.