# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 4 Experiments
### Experimental Results and Analysis  
Compared Methods  
In this section, we present a thorough comparison between the performance of FGCM and several state-of-the-art methods on the four commonly used benchmark datasets. Table 2 presents the evaluation results of FGCM and other methods on CIFAR-10 and CIFAR-100 under different ratios of symmetric noise based on PreAct-18 resnet. Meanwhile, Table 3 shows the results with different ratios of asymmetric noise. Here we briefly introduce some of the SOTA methods we compared:  
* **DivideMix. ** DivideMix trains two networks simultaneously and iteratively divides samples into either clean or noisy sets via a two-component mixture model. Furthermore, semi-supervised learning is performed with the clean and noisy set treated as labeled and unlabeled data, respectively. * **ELR. ** Using semi-supervised learning techniques, ELR first estimates target probabilities base on the outputs of model in the early training stage. Then, ELR hinders the memorization of noisy labels by employing a regularization term to maximize the inner product between the targets and model outputs. * **MOIT+. ** Networks are first pretrained with supervised contrastive learning. Samples are divided based on the learned features. Then a classifier is trained in a semi-supervised manner on the divided datasets. * **Sel-CL+. ** The training of Sel-CL+ is warmed up with unsupervised contrastive learning. With the obtained low-dimensional representations, Sel-CL+ selects confident sample pairs and trains the models with supervised contrastive learning technique. As stated in Table 2, Co-teaching+ gains a significant improvement from Cross-Entropy by excluding big-loss samples from training. However, the performance is limited compared with other methods due to discarding too many training samples. MOIT+ outperforms Co-teaching by a large margin. Leveraging supervised contrastive learning technique, MOIT+ learns a more robust representation and significantly alleviates the effect of noisy labels. Sel-CL+ gains further improvements by selecting confident sample pairs instead of clean samples. In this way, the learned representations benefit from not only the pairs with correct annotations, but also the pairs which are false-labeled from the same class. Therefore, the performance of Sel-CL+ is remarkably better than MOIT+ on high noise ratio settings (i.e., 80% symmetric noise on CIFAR-10 and CIFAR-100). DivideMix achieves comparable performance with Sel-CL+ on CIFAR-10 and CIFAR-100 with symmetric noise, owing to treating detected noisy samples as unlabeled data for semi-supervised training. However, the performance is not optimal under asymmetric noise as presented in Table 3, indicating that the small loss strategy does not work well for asymmetric noise. FGCM, our proposed methods, sets a new SOTA performance across all noise types and ratios. With the confidence of maximum repeating pseudo labels, FGCM can effectively perceive the difficulty experienced by DNN models when learning the samples, while with the confidence trends of annotation labels FGCM is able to decide whether a sample is clean. Combining