# Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
## 4 Discussion
It is, therefore, essential to consider the uncertainty associated with a model's predictions, as it can provide valuable insights into the reliability of its results and potential areas for improvement. Failure to account for such uncertainty can lead to overconfidence in a model's predictions, which can be detrimental in critical applications such as healthcare. Researchers and practitioners must be aware of the presence of uncertainty and focus on developing methods to quantify and account for it. By doing so, we can improve the overall reliability and robustness of deep learning models and ensure their effective application across a wide range of fields;
* In the field of machine learning, it is commonly held that using fully Bayesian methods in deep learning might not always lead to better results compared to approximate methods. This is due to the fact that fully Bayesian methods may not be effective with all types of data, particularly in the case of image data, where images can vary significantly from one another. In addition, using fully Bayesian methods can be computationally demanding, especially with models that have a large number of parameters to learn. Therefore, when deciding between fully Bayesian and approximate methods, it is important to consider factors such as the size of the data, the computing resources available, and the desired level of model complexity. Considering these factors can aid in selecting the most appropriate method for each specific task. Consequently, full BDL has not received widespread acceptance among machine learning researchers, particularly in the field of computer vision;
* The MLP-Mixer is known for its relatively limited interaction with neighboring neurons compared to CNNs. This limited interaction can result in decreased performance when working with smaller datasets, as the model may struggle to capture the complex patterns and relationships present in the data. However, for very large datasets, MLP-Mixer can perform equally well as CNN models while being more computationally efficient. Therefore, it is important to carefully consider the strengths and limitations of MLP-Mixers when choosing an architecture for a given task, especially when working with smaller datasets or in environments with limited computational resources. Researchers have developed different versions of MLP-Mixer to increase interaction between neighboring regions while decreasing interaction between further away regions [10]. Our findings suggest that incorporating uncertainty quantification using BDL can significantly enhance the performance of MLP-Mixers, particularly for small and medium-sized datasets;
* Unlike CNN models that use filters for uncertainty quantification, MLP-Mixers utilize regions of images or feature maps, offering a more comprehensive analysis of the data. This approach provides a more accurate representation of uncertainty, which can lead to better performance in certain applications;
* Although BDL often results in high estimated uncertainty for incorrectly assigned classes, it may also produce high uncertainty for correctly assigned classes as well. This phenomenon is particularly common among classes that are closely related. For example, in the context of cancer classification (Table 2), the normal and benign classes may both have high uncertainty, while the malignant class has low uncertainty. Alternatively, the benign and malignant classes may both exhibit high uncertainty, while the normal class has low uncertainty. However, it is less common for both the normal and malignant classes to show high uncertainty, while the benign class has low uncertainty, as these two classes possess noticeably distinct features that distinguish them from each other. Therefore, understanding the underlying factors that contribute to the uncertainty estimates of a model is crucial for interpreting and evaluating its performance.