## 2 Related Work
### Disagreement and Agreement  
The agreement strategy is inspired by semi-supervised learning, and JoCOR proposes the agreement maximization principle, which encourages two different classifiers to make closer predictions through an explicit regularization method. The samples that are considered to be consistent in the predictions of the two networks are more credible, and the consistency of the network predictions should be promoted as much as possible. The method is to add js divergence between the two network prediction results to judge the distance between the two network prediction results.  
Disagreement was proposed by Decoupling, and its key idea is to decouple \"when to update\" from \"how to update\". In previous studies, the network was updated all the time. For the \"Disagreement\" strategy, \"when to update\" depends on the Disagreement between the two networks rather than all the time. Co-teaching+ combines the \"Disagreement\" strategy with the cross-update on Co-teaching and proposes that the prediction divergence  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Baselines** & **Description** \\ \hline F-correction & F-correction corrects the label predictions by the label transition matrix. \\ \hline Decoupling & Decoupling only uses instances with different predictions from the two classifiers to update the parameters. \\ \hline Co-teaching & Co-teaching trains two networks simultaneously and let them cross-update. \\ \hline Co-teaching+ & Co-teaching+ trains two networks simultaneously and lets them cross-update when the two networks predict disagreement. \\ \hline JoCOR & JoCOR trains two networks at the same time and sets a contrast loss between the two networks to facilitate the two grids to reach an “Agreement”. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference baseline and its description.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Decoupling** & **Co-Teaching** & **Co-Teaching+** & **JoCOR** & **JoSDW** \\ \hline Small loss & X & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Cross update & X & \(\surd\) & \(\surd\) & X & X \\ \hline Disagreement & \(\surd\) & X & \(\surd\) & X & \(\surd\) \\ \hline Agreement & X & X & X & \(\surd\) & \(\surd\) \\ \hline Dynamic weight & X & X & X & X & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of baseline.  
of the two networks can better help network training and improve the robustness of the network. Therefore, Co-teaching only exchanges and updates the parts with inconsistent predictions in the small loss samples. In this study, we combine \"Agreement\" and \"Disagreement\", the \"Agreement\" part is called the pure sample, and the \"Disagreement\" part is called the complex sample, and dynamic weights are set for the complex sample.  
We strongly agree with maximizing the agreement, which combined with small loss samples results in pure samples that are cleaner than small loss samples. For the complex sample in the early stage of network model training, it can help the network to train better. This solves the problem of \"when to update\". However, with the training of the network model, the complex samples may no longer be a good guide for model updating.