# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 4 Experiments
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**CIFAR-10**} & \multicolumn{3}{c}{**CIFAR-100**} \\ \hline Noise type & \multicolumn{3}{c}{Sym} \\ \hline Method/Noise ratio & 20\% & 50\% & 80\% & 20\% & 50\% & 80\% \\ \hline Cross-Entropy & 86.8 & 79.4 & 62.9 & 61.8 & 37.3 & 8.8 \\ \hline Fcorr [20] & 86.8 & 79.8 & 63.3 & 61.5 & 46.6 & 19.9 \\ \hline Co-teaching+ [18] & 89.5 & 85.7 & 67.4 & 65.6 & 51.8 & 27.9 \\ \hline Pcorr [38] & 92.4 & 89.1 & 77.5 & 69.4 & 57.5 & 31.1 \\ \hline FINE [39] & 91.0 & 87.3 & 69.4 & 70.3 & 64.2 & 25.6 \\ \hline Meta-Learning [40] & 92.9 & 89.3 & 77.4 & 68.5 & 59.2 & 42.4 \\ \hline Mcorr [8] & 94.0 & 92.0 & 86.8 & 73.9 & 66.1 & 48.2 \\ \hline DivideMix [19] & 95.2 & 94.2 & 93.0 & 75.2 & 72.8 & 58.3 \\ \hline ELR [15] & 93.8 & 92.6 & 88.0 & 74.5 & 70.2 & 45.2 \\ \hline MSLC [41] & 93.5 & 90.5 & 69.9 & 72.5 & 68.9 & 24.3 \\ \hline MOIT+ [28] & 94.1 & 91.8 & 81.1 & 75.9 & 70.6 & 47.6 \\ \hline Sel-CL+ [12] & 95.5 & 93.9 & 89.2 & 76.5 & 72.4 & 59.6 \\ \hline FGCM & 95.6 & 94.9 & 94.1 & 77.1 & 74.9 & 61.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison with state-of-the-art methods on CIFAR-10 and CIFAR-100 with different ratios of symmetric noise as PreAct-18 resnet being the backbone. the two together, the proposed FGCM is able to mine more reusable supervision information, compared with DivideMix and Sel-CL+, from noisy datasets by categorizing training samples into fine-grained clusters. The improvement is significant especially for high noise ratios, which is more challenging. Sel-CL+ achieves competitive performance for low noise ratios (i.e., asymmetric noise and 20% symmetric noise) yet FGCM outperforms Sel-CL+ by a large margin for high noise ratios (e.g., 5.5% for 80% symmetric noise on CIFAR-10). This is owed to the ability of FGCM to accurately correct labels for noisy samples. While the performance of many sample selection methods degrades on the asymmetric noise, FGCM outperforms DivideMix by 2.4% for 40% asymmetric noise on CIFAR-10. For asymmetric noise, the training difficulties of different classes vary greatly due to the different degree of similarities between classes, causing the losses of samples in some annotated classes are generally higher than the other classes. Therefore, it is not convincing to realize the division of samples only based on the loss of their annotation labels. FGCM, however, is able to discriminate between hard samples and noisy samples by the fine-grained categorization, thus still achieving SOTA performance for asymmetric noise. Notably, the results of original DivideMix and ELR are acquired by using the average predictions of two trained models while other methods use only one. For a fair comparison, we report their results without model ensembles. The reported results of DivideMix+ and ELR+ are with model ensembles however FGCM still archives comparable or even better performance. The results demonstrate the effectiveness of our proposed FGCM. Figure 3 illustrates some of mislabeled images in CLothing1M and WebVision filtered and rectified by FGCM. Images are randomly chosen from the NS cluster generated by FGCM. The red labels above each image are the annotation labels from corresponding datasets while the green label below each image is the pseudo label produced by FGCM. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **TCNet [13]** & **Meta-Cleaner [42]** & **ELR** & **FINE** & **MSLC** & **Divide-Mix+** & **ELR+** & **FGCM** \\ \hline Accuracy & 71.15 & 72.50 & 72.87 & 72.91 & 74.02 & 74.76 & 74.81 & 74.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with state-of-the-art methods on clothing1M.