## 1 Introduction
[8] assigns different weights to input images. Images whose labels are assigned with low probability are given decreased weight in subsequent iterations of the algorithm and thus effectively cease to participate in the learning process. In more recent work, [PERSON] et al. [9] presented an iterative cross-validation method that determines which images within the training dataset should be removed in the next iteration. The authors show that the accuracy of a DNN trained with such iterative pruning leads to accuracy improvements. However, as for the methods outlined below, this approach does not determine whether the removed images were incorrectly labeled or whether they showed some outlying, abnormal, or atypical content, which confuses the DNN and reduces its accuracy when applied to the validation set. Very recently, [PERSON] et al. [10] proposed confidence learning (CL). As in [9], the authors use N-fold cross-validation but performed the procedure only once, rather than iteratively. Probability thresholds are determined for each class independently. Such thresholds determine if a given sample is classified confidently. The probabilities are obtained from the final softmax layer of a DNN. Images within the training dataset that are classified inaccurately but confidently are considered erroneously labeled and thus removed from the dataset. The newly filtered set is then used to prepare the final DNN model. The authors of the CL method tested its effectiveness using the ResNet-50 architecture with moderately high mislabel probabilities of 20-70%. Unfortunately, the number of erroneous images that were removed from the dataset was not logged. As in other work, the approach focuses on the increase in the classification performance of the final DNN model, which was shown to be higher than all the other methods reviewed here. The CL method has been used to analyze known databases of images [3]. The researchers selected suspicious images using this approach and then manually verified the images to detect incorrect labels. They determined that 0.15-10% of all images are mislabeled. This is an important finding, given that the analyzed reference databases should be free of such faults. This highlights the strong competition between algorithms that has led to them achieving very high classification accuracy, often exceeding 99%. In this study, the final number of incorrect labels was a relatively small fraction of the initial CL predictions. This indicates that the performance of the label noise detection can be improved by further balancing precision versus recall. The type of noise used throughout the literature is also important to investigate. Authors typically use a confusion matrix model. Such a model assumes a label has a certain probability of being replaced by a different label, depending on the class of the object in the image. This type of noise model is known as class-dependent noise (CDN). The proposed algorithmic solutions assume the presence of such predefined noise and attempt to reconstruct the noise model. In this manner, a significant improvement in learning is achieved. This noise model was adopted, e.g., in [7; 9; 10]. Uniform noise (UNI) is a much simpler noise model used throughout the literature. In this model, label errors occur independently from the class of object in the corresponding image. Only the fraction of altered labels changes. Such simple random permutation of labels was used, e.g., in [5; 6; 8]. [PERSON] et al. [11] demonstrated that instance-dependent noise (IDN) is present in real image databases. The probability of a labeling error within a dataset depends not only on the class of object in the image but also on the image characteristics. For example, consider an image of the written number \"1\". When skewed away from a vertical orientation, this could be more easily mistaken for the written number \"7\". Unfortunately, previous work on the topic has not accounted for this highly realistic form of noise.