# A New Image Oversampling Method Based on Influence Functions and Weights
## 4 Results and Discussion
### Experimental Results and Analysis of CIFAR Datasets  
In the CIFAR10 and CIFAR100 dataset experiments, we first process the dataset to be characterized by a long-tailed imbalanced distribution with an imbalance ratio of \(\rho=10,\rho=100\), which is compared with the CB [24], LDAM [25], LDAM-DRM [25], IB-Loss [19], and IB-Loss+Facal [19] schemes, respectively. Then, it is compared with GAN-based and its improved methods, including mixSMOTE [29], AWGAN [34], SIAMOTE [35], STGAN [36], and Few-Shot GAN [37]. The results are shown in Table 1.  
In Table 1, we make multiple comparisons experiments, and it can be observed that in the CIFAR datasets with imbalance ratios of 10 and 100, our scheme is better than the CB, LDAM, and IB-Loss. In the CIFAR-10 dataset with an imbalance ratio of 10, the accuracy of our scheme is 88.50%, which is very close to the accuracy of the IB-Loss scheme. Our scheme combines the oversampling method and the IB-Loss scheme, which can alleviate the sample imbalance problem and mitigate sample effects that lead to overfitting decision boundaries. Our scheme of the accuracy increased to 93.13%, and we see a significant improvement. In the CIFAR-100 dataset with an imbalance ratio of 10, our solution improves by 5% compared to other solutions, demonstrating the superiority of our solution. The imbalance rate of the dataset ranges from 10 to 100. We can see that the accuracy of all schemes is declining, and our scheme is still higher than other schemes in accuracy. Among them, the CB scheme has the lowest accuracy in each of the comparisons, mainly due to the fact that CB measures whether there is any overlap in the data by associating each sample with a small neighborhood instead of individual sample points. This scheme is sensitive to the choice in parameters. It is highly dependent on the distribution of the original data, which leads to abysmal performance for the unbalanced data since \begin{table}
\begin{tabular}{l l l l l} \hline \hline Method & CIFAR-10 & \multicolumn{3}{c}{CIFAR-100} \\ \hline Imbalance Ratio & 10 & 100 & 10 & 100 \\ \hline CB [24] & 87.49 & 74.57 & 57.99 & 39.60 \\ LDAM [25] & 86.96 & 73.35 & 56.91 & 39.65 \\ LDAM-DRW [25] & 88.16 & 77.03 & 57.99 & 42.04 \\ IB-Loss [19] & 88.25 & 78.26 & 57.13 & 42.14 \\ IB-Loss+Facal [19] & 88.04 & 79.76 & 58.20 & 42.06 \\ mixSMOTE [38] & 88.07 & 78.28 & 58.48 & 42.34 \\ AWGAN [34] & 90.14 & 77.61 & 62.25 & 48.73 \\ SIA-SMOTE [35] & 86.50 & 76.63 & 59.04 & 37.86 \\ STGAN [36] & 91.35 & 79.13 & 64.39 & 44.28 \\ Few-Shot GAN [37] & 89.47 & 75.87 & 60.55 & 41.05 \\ \hline Ours method & **93.13** & **79.86** & **66.50** & **50.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy in the imbalanced CIFAR datasets (%). Figure 6: Oversampling in the CIFAR-10 image dataset. the unbalanced data are originally distributed unbalanced. The LDAM scheme copes with category imbalance by adjusting the decision boundary so that the model focuses more on a small number of categories of samples, and it performs better in the CIFAR10 dataset and better in the CIFAR100 dataset; the accuracy of the scheme decreases rapidly, mainly due to the increase in the number of data categories, which makes the sample boundaries between the categories overlap more, leading to a decrease in performance. In our scheme, the accuracy is improved to 93.13% in the CIFAR10 dataset with an imbalance ratio of 10. However, the performance of AWGAN is slightly lower than that of our scheme using the GAN-based approach. The main reason for this is because the method relies on sample boundaries. There are various kinds of overlap (worse) and coverageof the different data categories in multiple categories, which reduces the practical performance of the scheme effect. The performance of several other GAN-based methods could be better; SIA-SMOTE is the least effective among the GAN-based methods.