# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Results and Discussion
#### 4.2.2 Results on Real Datasets
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Noise Type**} & \multicolumn{2}{c}{**Symmetric**} & \multicolumn{2}{c}{**Pair flip**} & \multicolumn{2}{c}{**Tridiagonal**} & \multicolumn{2}{c}{**Instance**} \\ \hline \multicolumn{2}{c}{**Setting**} & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** \\ \hline \multirow{10}{*}{SVHN} & CE & 87.44 \(\pm\) 0.34 & 67.09 \(\pm\) 0.56 & 83.42 \(\pm\) 0.43 & 60.86 \(\pm\) 0.37 & 86.08 \(\pm\) 0.24 & 67.21 \(\pm\) 0.44 & 83.27 \(\pm\) 0.45 & 59.55 \(\pm\) 0.64 \\  & GCE & 76.57 \(\pm\) 0.28 & 60.82 \(\pm\) 0.30 & 77.83 \(\pm\) 0.24 & 62.59 \(\pm\) 0.35 & 78.06 \(\pm\) 0.19 & 62.98 \(\pm\) 0.27 & 75.87 \(\pm\) 0.26 & 60.12 \(\pm\) 0.34 \\  & APL & 77.46 \(\pm\) 0.31 & 61.05 \(\pm\) 0.46 & 75.62 \(\pm\) 0.45 & 59.96 \(\pm\) 0.47 & 76.06 \(\pm\) 0.34 & 60.53 \(\pm\) 0.41 & 75.27 \(\pm\) 0.44 & 59.83 \(\pm\) 0.45 \\  & ELR & 74.39 \(\pm\) 0.28 & 56.19 \(\pm\) 0.23 & 76.94 \(\pm\) 0.22 & 57.43 \(\pm\) 0.26 & 75.94 \(\pm\) 0.28 & 57.16 \(\pm\) 0.21 & 75.69 \(\pm\) 0.32 & 58.16 \(\pm\) 0.27 \\  & MSLC & 97.08 \(\pm\) 0.17 & 95.12 \(\pm\) 0.14 & 96.97 \(\pm\) 0.21 & 95.24 \(\pm\) 0.23 & 97.10 \(\pm\) 0.18 & 96.88 \(\pm\) 0.20 & 97.09 \(\pm\) 0.23 & 96.95 \(\pm\) 0.25 \\  & CDR & 82.46 \(\pm\) 0.21 & 67.50 \(\pm\) 2.09 & 82.35 \(\pm\) 1.39 & 60.41 \(\pm\) 2.15 & 84.79 \(\pm\) 1.00 & 64.59 \(\pm\) 1.44 & 81.96 \(\pm\) 2.30 & 60.71 \(\pm\) 3.33 \\  & PES & 87.63 \(\pm\) 0.23 & 69.31 \(\pm\) 0.37 & 88.92 \(\pm\) 0.25 & 70.26 \(\pm\) 0.31 & 89.17 \(\pm\) 0.19 & 70.34 \(\pm\) 0.27 & 85.49 \(\pm\) 0.21 & 67.38 \(\pm\) 0.36 \\  & MLC & 96.75 \(\pm\) 0.06 & 95.96 \(\pm\) 0.12 & 96.99 \(\pm\) 0.11 & 96.06 \(\pm\) 0.10 & 96.43 \(\pm\) 0.09 & 95.81 \(\pm\) 0.11 & 96.38 \(\pm\) 0.12 & 95.24 \(\pm\) 0.15 \\  & CTRR & 96.21 \(\pm\) 0.14 & 95.57 \(\pm\) 0.53 & 97.02 \(\pm\) 0.21 & 96.20 \(\pm\) 0.34 & 96.53 \(\pm\) 0.44 & 96.06 \(\pm\) 0.45 & 96.17 \(\pm\) 0.14 & 95.89 \(\pm\) 0.11 \\  & FPL & **97.14 \(\pm\) 0.02** & **96.87 \(\pm\) 0.02** & **97.15 \(\pm\) 0.01** & **97.11 \(\pm\) 0.02** & **97.14 \(\pm\) 0.01** & **97.01 \(\pm\) 0.01** & **97.11 \(\pm\) 0.01** & **97.02 \(\pm\) 0.01** \\ \hline \multirow{10}{*}{CIFAR10} & CE & 83.81 \(\pm\) 0.15 & 66.22 \(\pm\) 0.43 & 81.83 \(\pm\) 0.32 & 59.41 \(\pm\) 0.60 & 82.97 \(\pm\) 0.23 & 64.18 \(\pm\) 0.41 & 81.65 \(\pm\) 0.42 & 59.02 \(\pm\) 0.42 \\  & GCE & 89.7 \(\pm\) 0.23 & 87.62 \(\pm\) 0.35 & 89.83 \(\pm\) 0.19 & 87.61 \(\pm\) 0.48 & 90.35 \(\pm\) 0.04 & 86.63 \(\pm\) 0.06 & 89.45 \(\pm\) 0.29 & 82.28 \(\pm\) 0.67 \\  & APL & 88.15 \(\pm\) 0.09 & 81.51 \(\pm\) 0.17 & 86.07 \(\pm\) 0.09 & 51.66 \(\pm\) 0.20 & 77.34 \(\pm\) 0.23 & 32.66 \(\pm\) 0.26 & 78.83 \(\pm\) 2.11 & 60.00 \(\pm\) 1.71 \\  & ELR & 89.42 \(\pm\) 0.09 & 87.52 \(\pm\) 0.15 & 89.87 \(\pm\) 0.11 & 89.36 \(\pm\) 0.14 & 88.96 \(\pm\) 0.1 & 88.48 \(\pm\) 0.17 & 89.78 \(\pm\) 0.08 & 89.05 \(\pm\) 0.17 \\  & MSLC & 93.20 \(\pm\) 0.09 & 90.67 \(\pm\) 0.17 & 94.09 \(\pm\) 0.09 & 92.74 \(\pm\) 0.11 & 92.82 \(\pm\) 0.11 & 92.44 \(\pm\) 0.11 & 92.46 \(\pm\) 0.1 & 91.93 \(\pm\) 0.14 \\  & CDR & 82.79 \(\pm\) 0.66 & 64.16 \(\pm\) 0.74 & 81.25 \(\pm\) 0.96 & 60.17 \(\pm\) 0.94 & 82.14 \(\pm\) 0.60 & 64.13 \(\pm\) 1.13 & 81.11 \(\pm\) 0.80 & 62.69 \(\pm\) 1.51 \\  & PES & 92.48 \(\pm\) 0.06 & 89.17 \(\pm\) 0.13 & 92.14 \(\pm\) 0.03 & 88.77 \(\pm\) 0.09 & 92.40 \(\pm\) 0.05 & 88.70 \(\pm\) 0.08 & 91.57 \(\pm\) 0.07 & 89.27 \(\pm\) 0.11 \\  & MLC & 89.46 \(\pm\) 0.12 & 86.53 \(\pm\) 0.24 & 90.33 \(\pm\) 0.15 & 90.30 \(\pm\) 0.14 & 90.31 \(\pm\) 0.15 & 90.29 \(\pm\) 0.13 & 89.74 \(\pm\) 0.13 & 89.51 \(\pm\) 0.14 \\  & CTRR & 92.97 \(\pm\) 0.32 & 92.16 \(\pm\) 0.31 & 93.05 \(\pm\) 0.56 & 92.82 \(\pm\) 0.27 &