# A New Image Oversampling Method Based on Influence Functions and Weights
###### Abstract  
Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so studying imbalanced data classification is of practical significance. We propose an image oversampling algorithm based on the influence function and sample weights. Our scheme not only synthesizes high-quality minority class samples but also preserves the original features and information of minority class images. To address the lack of visually reasonable features in SMOTE when synthesizing images, we improve the pre-training model by removing the pooling layer and the fully connected layer in the model, extracting the important features of the image by convolving the image, executing SMOTE interpolation operation on the extracted important features to derive the synthesized image features, and inputting the features into a DCGAN network generator, which maps these features into the high-dimensional image space to generate a realistic image. To verify that our scheme can synthesize high-quality images and thus improve classification accuracy, we conduct experiments on the processed CIFAR10, CIFAR100, and ImageNet-LT datasets. data imbalance; image oversampling; SMOTE interpolation; DCGAN +
Footnote †: journal: Applied  
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Footnote 1: email: {https://doi. data to improve recognition performance poses a huge challenge to modern deep learning methods. Therefore, studying multi-category long-tail imbalanced data classification has important practical significance. Solving the learning and classification problem for unbalanced data is shown in Figure 1. The relevant research programs can be explored from two directions. (1) Algorithm level: Algorithm-level methods focus on minority class samples by modifying classifiers, setting weights, improving loss functions, etc. Or, adjust the training process of the model through the penalty mechanism and improve the training strategy for small class samples. However, these methods lack flexibility and are prone to overfitting of the model. (2) Data level: The main methods include undersampling and oversampling [7], and hybrid sampling that combines these two sampling methods. These methods start from the perspective of data and use various methods to achieve a theoretical balance of various types of data, thereby solving the problem of unbalanced data. These methods are efficient and easy to understand, and they can also retain the original feature information of the data. Oversampling is considered to solve the problem of data imbalance. We begin at the data level by studying data oversampling algorithms to mitigate dataset imbalance by generating minority-class images. A random oversampling algorithm is a simple approach that increases the number of minority-class images by randomly replicating them until the class balance is achieved. However, excessive replication in this algorithm confines the minority-class images to a limited region, leading to model overfitting. A synthetic oversampling technique is proposed to address this issue. This algorithm selects K-nearest samples from the minority class and then randomly synthesizes new samples along the connecting lines between these points to alleviate data imbalance. Although the Synthetic Minority Oversampling Technique (SMOTE) algorithm is a classic and widely used method, it has several drawbacks. The main issues are as follows. (1) Oversampling image with less information. In oversampling, since the samples with less information are far away from the decision boundary of the data category, the contribution of these samples to the classifier is limited. Oversampling the samples with less information balances the dataset in terms of quantity, but the improvement in the classifier accuracy is limited.