# Cras: Curriculum Regularization and Adaptive Semi-Supervised Learning with Noisy Labels
## 3 Proposed Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Train** & **Val** & **Test** & **Image Size** & **Classes** \\ \hline \multicolumn{6}{c}{Simulated datasets with clean annotations} \\ \hline CIFAR-10 & 50 k & - & 10 k & 32 \(\times\) 32 & 10 \\ CIFAR-100 & 50 k & - & 10 k & 32 \(\times\) 32 & 100 \\ \hline \multicolumn{6}{c}{Datasets with real world annotations} \\ \hline Clothing1M & 1M & 14 k & 10 k & 224 \(\times\) 224 & 14 \\ WebVision1.0 & 66 k & 2.5 k & - & 256 \(\times\) 256 & 50 \\ ILSVRC12 & - & 2.5 k & - & 256 \(\times\) 256 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the datasets used in our experiments. experiments, the real-world datasets chosen were ranked as the top two with respect to noise rate, according to the latest survey findings [60]. Thus, it is important to emphasize that the chosen datasets serve as carefully selected benchmarks, with respect to complexity and size, for assessing the performance of our CRAS. For the Clothing1M dataset, a ResNet-50 network pretrained on ImageNet was used, following the approach of previous work [56]. For the mini-WebVision dataset, an Inception-ResNet V2 model was employed [61]. The CR period was 3 epochs. The hyperparameters for these experiments were set to \(m=0.01\) and \(\tau=0.7\). By evaluating CRAS on these real-world datasets, the effectiveness of the method for handling noisy labels and improving network performance in practical scenarios could be demonstrated. The results of these evaluations provide further evidence for the potential of CRAS as a robust method for LNL in real-world applications. #### 4.1.3 Comparative Methods

CRAS was compared with multiple baseline methods, including state-of-the-art methods for handling noisy labels in supervised learning tasks. Following [25], the baseline methods for the comparison, in addition to those discussed in Section 2, include the methods listed below:

* PENCIL [34]: A method that iteratively updates class probabilities and refines noisy labels by minimizing the difference between the estimated class distribution and the class distribution predicted by the network. * Joint-Optim [33]: A method that estimates true labels from network predictions and relabels samples for explicit loss correction. * Iterative-CV [20]: A method that iteratively applies cross-validation to noisy labeled datasets to improve label quality and model performance. * CORES [62]: A method that uses a data reweighting mechanism and an iterative learning process to identify and reweight clean and noisy samples, thereby enhancing the learning process. By comparing CRAS with these methods, the relative performance and effectiveness of CRAS in handling noisy labels could be assessed. A successful comparison would demonstrate the advantages of CRAS with respect to its ability to handle noisy labels, robustness to different noise rates, and improved generalization performance. ### Results on Simulated Noisy Datasets

Table 2 shows the results of the comparison between CRAS and the state-of-the-art methods on CIFAR-10 and CIFAR-100, with different proportions of symmetric and asymmetric noise. Best and Last are standard metrics that are commonly used to evaluate model performance and stability in deep learning, respectively [23; 25; 33]. Best is the highest accuracy that the model achieved during training; this indicates the peak performance of the model. Last is the average accuracy over the final 10 epochs; this indicates the robustness of the model to noisy labels toward the end of training. For CRAS and LongReMix, the standard deviation is also shown. On CIFAR-100, CRAS outperformed DivideMix across all noise rates and noise patterns. It also outperformed the state-of-the-art LongReMix method in noise experiments with the exception of 90% of symmetric noise. In particular, on CIFAR-100 with 20%, 50%, and 80% symmetric noise, CRAS outperformed the Best of the state-of-the-art LongReMix by 1.6%, 1.5%, and 2.7%, respectively.