# An Explainable Scheme for Memorization of Noisy Instances by Downstream Evaluation
## 4 Experimental Setup and Results
### Experimental Results  
The outcomes of error rates shown from experimental results are as listed in Tables 1-6. It is clear that the error rate generally increases with the increasing intensity of the noise. Among the images correctly classified in the original data without noise, it can be seen that the error rates E/A with normal noise are consistently lower than those with uniform noise across all noise intensities. This is believed to be due to the smaller standard deviation of normal samples compared to the uniform ones, more extreme noise values from normal samples lead to misclassifications by the model. In addition, the error rates for CIFAR-10 are generally higher than those for STL-10, as shown in Tables 1-4, likely due to the poorer image resolution of CIFAR-10. Furthermore, the error rates for CIFAR-100 (as shown in Tables 5 and 6) are noticeably higher than other datasets, which can be attributed to the increased number of classes, while the number of training epochs and the total data amount remain the same, as in CIFAR-10, leading to a decline of accuracy in model classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\beta\) & **S** & **E on S** & **E/S** & **A** & **E on A** & **E/A** \\ \hline
0 & 5000 & 34 & 0.68\% & – & – & – \\ \hline
0.1 & 5000 & 30 & 0.60\% & 4966 & 2 & 0.04\% \\ \hline
0.2 & 5000 & 33 & 0.66\% & 4966 & 9 & 0.18\% \\ \hline
0.5 & 5000 & 41 & 0.82\% & 4966 & 22 & 0.44\% \\ \hline
0.7 & 5000 & 64 & 1.28\% & 4966 & 42 & 0.85\% \\ \hline
1.0 & 5000 & 93 & 1.86\% & 4966 & 79 & 1.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: STL-10 with noises sampled from normal distribution. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\beta\) & **S** & **E on S** & **E/S** & **A** & **E on A** & **E/A** \\ \hline
0 & 5000 & 50 & 1.00\% & – & – & – \\ \hline
0.1 & 5000 & 56 & 1.12\% & 4950 & 19 & 0.38\% \\ \hline
0.2 & 5000 & 51 & 1.02\% & 4950 & 30 & 0.61\% \\ \hline
0.5 & 5000 & 80 & 1.60\% & 4950 & 60 & 1.21\% \\ \hline
0.7 & 5000 & 120 & 2.40\% & 4950 & 99 & 2.00\% \\ \hline
1.0 & 5000 & 233 & 4.66\% & 4950 & 211 & 4.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: STL-10 with noises sampled from uniform distribution. In the observations of the visualized memorization matrices as shown in Figures 2-7, red regions represent areas with high memorization scores, indicating that the model has less impression on the features within these regions for associating this data with its supervised label during the training process, and thus, might misclassify into other classes. Conversely, blue regions represent regions with low memorization scores, indicating that the model has high confidence in reasoning the features within these regions based on the learning process of pre-training tasks. In these samples, originally correctly \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\beta\) & **S** & **E on S** & **E/S** & **A** & **E on A** & **E/A** \\ \hline
0 & 10,000 & 5411 & 54.11\% & — & — & — \\ \hline
0.1 & 10,000 & 4656 & 46.56\% & 4589 & 461 & 10.05\% \\ \hline
0.2 & 10,000 & 4417 & 44.17\% & 4589 & 809 & 17.63\% \\ \hline
0.5 & 10,000 & 3599 & 35.99\% & 4589 & 679 & 14.80\% \\ \hline
0.7 & 10,000 & 3377 & 33.77\% & 4589 & 638 & 13.90\% \\ \hline
1.0 & 10,000 & 3506 & 35.06\% & 4589 & 790 & 17.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CIFAR-100 with noises sampled from uniform distribution. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\beta\) & **S** & **E on S** & **E/S** & **A** & **E on A** & **E/A** \\ \hline
0 & 10,000 & 2805 & 28.05\% & — & — & — \\ \hline
0.1 & 10,000 & 2514 & 25.14\% & 7195 & 405 & 5.63\% \\ \hline
0.2 & 10,000 & 2298 & 22.98\% & 7195 & 572 & 7.95\% \\ \hline
0.5 & 10,000 & 1719 & 17.19\% & 7195 & 447 & 6.21\% \\ \hline
0.7 & 10,000 & 1633 & 16.33\% & 7195 & 524 & 7.28\% \\ \hline
1.0 & 10,000 & 1784 & 17.84\% & 7195 & 729 & 10.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CIFAR-10 with noises sampled from uniform distribution.