# A New Image Oversampling Method Based on Influence Functions and Weights
###### Abstract
The SMOTE scheme randomly selects samples for oversampling, which will cause the synthetic samples to be far away from the decision boundary and contain only a little helpful information. After oversampling, the learning effect of the classifier cannot be improved well. Figure 1: Imbalance data resolution. (2) Generate noise samples. Noise is an outlier, usually in the majority class sample area or near the decision boundary. Usually, noise is caused by the unreasonable selection of the number of k, which leads to synthetic anomalies and data. In the SMOTE scheme, since k is determined before oversampling, more noise may be generated due to the dataset's noise and the data distribution's irregularity. In order to solve the problem of imbalance in the image dataset, based on the idea of image importance, we further improve the SMOTE algorithm so that it can deal with image data and ensure the quality of the synthesized image. The influence function is used in the algorithm to calculate the sensitivity of the model parameters to the sample weights and linearly calculate with the weights to derive the sample score; the more significant the score, the more critical it is for images. The algorithm first selects the initial image and calculates the image with the higher score as the initial image through screening, then synthesizes a few classes of image features according to the improved SMOTE interpolation, inputs the synthesized features into the model, restores the features to the original image through Deep Convolutional Generative Adversarial Networks (DCGANs), calculates the score of each synthesized image, and retains the samples with more significant scores to improve the quality of the synthesized image. This paper hypothesizes that combining the influence functions with a DCGAN-based image generator will yield visually realistic and classifier-effective synthetic samples, thus outperforming traditional SMOTE methods on imbalanced datasets. The contribution of this paper is summarized as follows. (1) We propose an image oversampling algorithm called IFW-SMOTE (Influence Function and Weights-based Synthetic Minority Oversampling Technique) based on the influence function and sample weights to mitigate the problem of data imbalance, and the scheme is divided into three stages: initial image sample selection, synthesized image features, and image filtering. (2) First, the minority class image samples are input into the pre-training model, the sensitivity of the model parameters to the weight of each minority class sample is calculated according to the influence function, the image samples are filtered and used as the initial samples, and then the new samples are synthesized according to the sample synthesis algorithm. In order to enable the SMOTE method to be applied to images, we improve the pre-training ResNet network by extracting the image features, oversampling the image features, and inputting the image features obtained from oversampling into the DCGAN generator to restore the image and generate a more realistic image. It is also necessary to calculate the score of the new synthetic image samples, filter the synthetic image, remove the noise images, and improve the quality of the synthetic images. (3) To verify the performance of our scheme in imbalanced data, we conduct experiments on the processed CIFAR-10, CIFAR-100, and ImageNet-LT datasets and conduct multiple sets of comparative experiments.