## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Net/Method**} & \multicolumn{3}{c}{**UNI**} & \multicolumn{3}{c}{**CDN**} & \multicolumn{3}{c}{**IDN**} \\ \cline{2-11}  & **5\%** & **10\%** & **20\%** & **5\%** & **10\%** & **20\%** & **5\%** & **10\%** & **20\%** \\ \hline \multicolumn{11}{c}{CIFAR10} \\ \hline SimpleDLA & 0.708 & 0.794 & 0.802 & 0.669 & 0.742 & 0.755 & 0.294 & 0.573 & 0.798 \\ PreActResNet & 0.764 & 0.811 & 0.833 & 0.715 & 0.764 & 0.799 & 0.344 & 0.562 & 0.79 \\ DLA & 0.72 & 0.781 & 0.818 & 0.693 & 0.747 & 0.771 & 0.363 & 0.584 & 0.781 \\ DPN & 0.733 & 0.791 & 0.804 & 0.711 & 0.736 & 0.741 & 0.255 & 0.511 & 0.773 \\ MobileNetV2 & 0.687 & 0.738 & 0.79 & 0.681 & 0.694 & 0.75 & 0.252 & 0.532 & 0.756 \\ RegNet & 0.724 & 0.791 & 0.778 & 0.712 & 0.744 & 0.737 & 0.292 & 0.559 & 0.781 \\ VGG & 0.742 & 0.786 & 0.8 & 0.693 & 0.752 & 0.74 & 0.306 & 0.575 & 0.779 \\ ResNeXt & 0.782 & 0.804 & 0.808 & 0.733 & 0.763 & 0.751 & 0.328 & 0.552 & 0.774 \\ MultiNET & **0.897** & **0.928** & **0.954** & **0.872** & **0.907** & **0.937** & **0.544** & **0.72** & **0.884** \\ \hline \multicolumn{11}{c}{CIFAR100} \\ \hline SimpleDLA & 0.379 & 0.474 & 0.571 & 0.299 & 0.414 & 0.541 & 0.1 & 0.201 & 0.424 \\ PreActResNet & 0.385 & 0.482 & 0.559 & 0.295 & 0.421 & 0.523 & 0.096 & 0.205 & 0.413 \\ DLA & 0.405 & 0.472 & 0.568 & 0.304 & 0.391 & 0.552 & 0.097 & 0.202 & 0.425 \\ DPN & 0.311 & 0.433 & 0.525 & 0.291 & 0.391 & 0.494 & 0.1 & 0.196 & 0.389 \\ MobileNetV2 & 0.327 & 0.421 & 0.514 & 0.266 & 0.343 & 0.504 & 0.109 & 0.189 & 0.406 \\ RegNet & 0.299 & 0.415 & 0.489 & 0.275 & 0.364 & 0.493 & 0.092 & 0.2 & 0.394 \\ VGG & 0.349 & 0.44 & 0.549 & 0.279 & 0.385 & 0.545 & 0.094 & 0.194 & 0.404 \\ ResNeXt & 0.343 & 0.472 & 0.552 & 0.323 & 0.409 & 0.549 & 0.107 & 0.208 & 0.423 \\ MultiNET & **0.554** & **0.64** & **0.723** & **0.458** & **0.567** & **0.704** & **0.139** & **0.26** & **0.535** \\ \hline \multicolumn{11}{c}{GTSRB} \\ \hline SimpleDLA & 0.995 & 0.995 & 0.993 & 0.993 & 0.996 & 0.992 & 0.949 & 0.967 & 0.975 \\ PreActResNet & 0.993 & 0.995 & 0.995 & 0.996 & 0.995 & 0.991 & 0.954 & 0.971 & 0.981 \\ DLA & 0.996 & 0.995 & 0.994 & 0.995 & 0.995 & 0.994 & 0.947 & 0.969 & 0.976 \\ DPN & 0.993 & 0.995 & 0.992 & 0.991 & 0.993 & 0.995 & 0.962 & 0.974 & 0.983 \\ MobileNetV2 & 0.996 & 0.992 & 0.992 & 0.995 & 0.992 & 0.992 & **0.969** & 0.967 & 0.98 \\ RegNet & 0.995 & 0.992 & 0.992 & 0.995 & 0.994 & 0.993 & 0.959 & 0.978 & 0.982 \\ VGG & 0.988 & 0.992 & 0.993 & 0.99 & 0.994 & 0.992 & 0.947 & 0.969 & 0.978 \\ ResNeXt & 0.99 & 0.994 & 0.991 & 0.991 & 0.995 & 0.991 & 0.968 & 0.975 & 0.981 \\ MultiNET & **1.0** & **0.997** & **0.997** & **0.997** & **0.997** & **0.998** & 0.965 & **0.979** & **0.986** \\ \hline \multicolumn{11}{c}{MNIST} \\ \hline SimpleDLA & 0.977 & 0.993 & 0.996 & 0.979 & 0.991 & 0.992 & 0.957 & 0.945 & 0.935 \\ PreActResNet & 0.98 & 0.993 & 0.996 & 0.987 & 0.994 & 0.992 & 0.957 & 0.958 & 0.922 \\ DLA & 0.992 & 0.987 & 0.997 & 0.989 & 0.997 & 0.991 & 0.967 & 0.947 & 0.934 \\ DPN & 0.987 & 0.996 & 0.995 & 0.971 & 0.991 & 0.996 & 0.949 & 0.979 & 0.94 \\ MobileNetV2 & 0.973 & 0.992 & 0.996 & 0.982 & 0.992 & 0.995 & 0.953 & 0.938 & 0.919 \\ RegNet & 0.965 & 0.985 & 0.996 & 0.989 & 0.992 & 0.969 & 0.947 & 0.951 & 0.938 \\ VGG & 0.993 & 0.992 & 0.996 & 0.987 & 0.995 & 0.995 & 0.965 & 0.917 & 0.925 \\ ResNeXt & 0.988 & 0.979 & 0.994 & 0.986 & 0.989 & 0.995 & 0.961 & 0.962 & 0.94 \\ MultiNET & **0.997** & **0.999** & **0.999** & **0.999** & **0.998** & **0.998** & **0.975** & **0.99** & **0.975** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Area under precision–recall curve (AUC) measure of noisy label detection efficiency. Best results are indicated in bold. The opposite is true for the MNIST database, wherein IDN noise caused a substantially greater decrease in classification performance than CDN and UNI noise. In this case, the written digits that were relabeled due to noise were very similar to those of different classes. These similarities can be observed in Figures 4 and 5.