## 3 Experimental Verification  
The primary experimental objective was to compare our novel flexible MultiNET algorithm with the original CL algorithm of [PERSON] et al. [10].  
In contrast to the majority of the literature, our focus was on the number of inaccurate labels that were successfully detected by the algorithm and the number of false positives which occurred. To our knowledge, no works exist which follow a similar line of investigation. An overview of the experiment is presented in Figure 1.  
Four popular image databases were used in the experiments: CIFAR10 [12], CIFAR100 [13], GTSRB [14], and MNIST [15]. The CIFAR10 and CIFAR100 databases contain images of different types, such as dog, cat, tree, car, etc. The GTSRD database contains images of 43 types of road sign, and the MNIST database contains handwritten numerals. The number of images and the number of classes within each database are given in Table 1.  
For use during the learning process, we divided each original training dataset into a new training set containing 80% of the images and a new verification set containing 20% of the images. The original verification set was used as an evaluation set to test the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Set** & **Verification Set** & **Classes** \\ \hline CIFAR10 & 50,000 & 10,000 & 10 \\ CIFAR100 & 50,000 & 10,000 & 100 \\ GTSRB & 39,209 & 12,630 & 43 \\ MNIST & 60,000 & 10,000 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of images and classes within each experimental dataset.  
Figure 1: Experimental schema. The original training dataset is divided into new DNN training and verification sets. The original test dataset is used to evaluate the ability of the algorithms to detect noisy labels.  
effectiveness of noisy label detection. Label noise was applied to all datasets according to the three noise models: UNI, CDN, and IDN. Three noise levels, defined as the percentage of noisy labels, were analyzed: 5%, 10%, and 20%. These noise values were chosen to reflect the levels of noise present in real databases.  
Consistency was maintained between the type and intensity of noise in both the learning set and the evaluation set. For example, a DNN trained on a set perturbed by UNI noise at a level of 10% was then applied to a verification set that was perturbed by the same type and level of noise. This approach was used because the type and level of noise should be constant across the entire image set, as the entirety of a given dataset is typically prepared by the same entity before being divided.  
We used eight state-of-the-art DNN architectures that currently achieve the highest classification efficiency on popular image databases. Details of the architectures are presented in Table 2. All operations were performed by the PyTorch Python library [16], using stochastic gradient descent (SGD) optimization [17] with cross-entropy loss.  
During each 200-epoch training phase, popular methods of training dataset augmentation were used to improve DNN efficiency. The value of 200 was taken as a safe number of epochs, since most networks stabilized their accuracies around 100-150 epochs. These methods included brightness normalization, image rotation (limited to 20 degrees for the MNIST database and not used for the GTSRB database to avoid the misinterpretation of road signs) and random cropping.  
The 200-epoch training phase of a single model took from 15 min (for the simplest models and MNIST or CIFAR10 databases) to 3 h (for most complex architectures and the CIFAR100 set) on a utilized NVidia A5000 24 GB graphic card. The classification of all images in any image dataset with any of the trained models took usually less then a minute on this GPU.