## 2 Proposed Method
### Construction of Feature Distortion Space  
In contrast to methods based on AutoAugment [36], the premise of the method proposed in this paper is to start with specified data augmentation strategies and then optimize their inner attributive parameters based on these strategies. Therefore, to search for the optimal distortion magnitudes, it is essential to construct an appropriate magnitude space. The preset distortion magnitude values are stored in registers, and as the iterative process proceeds, the current parameters are continuously updated based on the loss value. The last updated magnitude parameters represent the best magnitude for feature enhancement. In the edge distortion section, we use simple operators (such as the Sobel operator) to first extract the edge information from the original image and then perform a dilation operation on the edges of the image. The size of the dilation matrix is \(D\times D\), which determines the degree of thickening of the shape edges. Based on empirical evidence, the range of \(D\) is set within \(D\in[1,8]\). This parameter will be involved in the learning process. For texture distortion, we refer to the six attributes of texture features proposed by [PERSON] et al. [37] (i.e., contrast, coarseness, directionality, regularity, linearity, and roughness, with the first three being more significant in feature representation). We use the addition of random pixel grains to control the roughness of the image. The parameter involved in the search process is the granularity level \(P\), with \(P\) representing the number of times random pixels are added. Before adding, we can set the number of pixels to be added and the size of the pixel blocks artificially. The benefit of this approach is that it simplifies the model and makes the enhancement magnitude controllable. To simplify the calculation in the color distortion section, we randomly (or at evenly spaced intervals) set \(C\) color combinations of RGB channels. In other words, we select values from the RGB channels to form \(C\) different combinations. In this section, there are \(C\) updatable values that determine the color. This approach allows us to establish a discrete distortion magnitude space, denoted as \(\Phi\) = (\(D\), \(P\), \(C\)). Assuming \(P\) = 20 and \(C\) = 10, this results in a potential pool of 8 \(\times\) 20 \(\times\) 10 different distortion magnitude combinations. It is worth noting that the aforementioned augmentation strategies may not necessarily represent the optimal choices, as there can be multiple strategies to choose from. Furthermore, these diverse strategies entail different parameters for representing the distortion magnitude. It is important to clarify that our research focuses on exploring the distortion magnitude space and does not encompass learning within the strategy space. During the exploration process, we employ a segmented search strategy by dividing the magnitude values for each feature into \(\rho\) subregions. The mean distortion magnitude from each subregion combination is introduced into the iterative process as the magnitude parameter. In each iteration, a set of distortion magnitudes for each feature is generated, which corresponds to a specific loss function, \(L_{\text{d}}\). Subsequently, within the subregion associated with the smallest \(L_{\text{d}}\), we conduct further searches. This subregion is then excluded, and the process is repeated iteratively. Figure 4 visually illustrates this process, where \(m\) denotes the index of the subspace. The best distortion magnitudes determined for \(X_{ij}\) during the current iteration are subsequently incorporated, replacing the original images in the support set for the next iteration. The value of \(\rho\) can be tailored to the size of the distortion magnitude space. Through this approach, we efficiently realize dynamic feature distortion selection while streamlining the search process, considerably reducing computational overhead in the iterations. Figure 4: Illustration of search process of distortion magnitude space.