# Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
## 4 Discussion  
The results presented in this study suggest that incorporating uncertainty quantification techniques, specifically BDL, can significantly enhance the performance of MLP-Mixers. This approach not only improves the model's accuracy but also enables quantification of the uncertainty associated with both the models and the data. However, it is crucial to note that while the results of this study have been compared with those of CNN models, there are other factors to consider when using uncertainty quantification that has not been explored in this research. To provide a more comprehensive understanding of the use of uncertainty quantification, it is essential to consider the following additional points. * In the context of BDL, it is frequently observed that a model's performance declines when the mean of the predicted true classes falls below a certain threshold, which in this study's datasets was around 50%. This situation is characterized by a decrease in the model's standard deviation, indicating a failure to capture the associated uncertainty. It is worth noting that this observation does not necessarily imply a failure of the BDL, but rather indicates that the model has failed to effectively generalize the results and predict each class accurately. In such cases, it is recommended to explore alternative methods or models that may be more suitable to the data and task at hand. This approach may involve adjusting the model's hyperparameters or selecting a different model architecture altogether. Additionally, it may be necessary to reconsider the data preprocessing steps or feature engineering techniques used in the model development process. Therefore, it is important to note that the identification of poor-performing models and the subsequent exploration of alternative approaches are crucial steps in the iterative model development process. It is through this iterative process that one can gain a better understanding of the data, model, and associated uncertainties, ultimately leading to improved model performance and increased confidence in the resulting predictions;
* In BDL, when a model is presented with unfamiliar data, the associated posterior distributions become more uncertain, resulting in higher uncertainty estimates. This increased uncertainty indicates that the network is less confident in its predictions, and it can serve as a useful tool for identifying abnormal or unusable data that significantly deviate from the training dataset. By utilizing the model uncertainty estimates to identify such data, BDL models, particularly MLP-Mixer models, can deliver improved performance and more reliable results across various applications. This approach to leveraging uncertainty estimates to detect abnormal data aligns with the wider concept of model-based anomaly detection, which has proven successful in various applications. Additionally, by quantifying the associated uncertainty, BDL models can provide a valuable tool for identifying data points that are on the edge of the model's training distribution. This can serve as a foundation for further exploring the data-generating process and improving the model's overall generalization ability;
* It is important to understand that a perfect score on a dataset does not necessarily imply perfect confidence or zero uncertainty in a model's predictions. Our research findings, as presented in Tables 1 and 2, demonstrate this fact. However, some publications have misinterpreted the relationship between a perfect score and uncertainty. It is important to note that the likelihood of a model having zero uncertainty or standard deviation is extremely low and that this situation can only occur under extreme circumstances. This highlights the fact that even when a model performs exceptionally well on a dataset, some degree of uncertainty remains in its predictions due to various factorssuch as model bias, incomplete information, and data variability.