Research On Bayesian Deep Learning Classification Model For Sample Diversit

In today’s rapidly developing information age,artificial intelligence technology and equipment have spread to all aspects of our lives.Deep learning is one of the latest trends in artificial intelligence research,and deep learning image classification methods are also extremely useful.Image classification forms the basis of other computer vision tasks such as positioning,detection,and segmentation.However,the image classification task still faces many challenges.The first is that in practical applications,the difference of the same object at different times will cause the deep learning classification model to deteriorate due to the increase of features.The second is that when we only use a single model to solve this problem,improving on a single strong model will not only make the model structure bigger,but the improved method is also a challenge.The third challenge is that the method of deepening the network structure is usually used to improve the classification accuracy.Due to the problem of gradient disappearance or explosion,this method is not necessarily effective,and the large structure also limits the use of the model on more devices.This paper constructs a new framework that increases the number of models and performs classification tasks by using multiple models instead of a single model.This method improves the classification accuracy by selecting more models that output different results.We have trained different models on different sets of appearance domains,and selected the most correct result among the output results of all models.Then by recursively using the Bayesian method on the selected model output,the problem of similarity between the models in the process is solved,and the uncertainty of the model is also increased.The framework of this paper ensures the diversity of models.In addition,we also collected a large number of data sets and performed data enhancements to emulate the changing appearance of objects in reality.We use the processed data set to train and validate the framework of our paper.This paper uses three different basic models to verify the proposed method on multiple data sets.Compared with the existing methods,the method in this paper has achieved a certain degree of improvement in accuracy.Then we also conducted ablation experiments.The experimental results show that our framework can achieve higher classification accuracy than a single model,and is more robust in the face of changing image appearance.In addition,since it is much easier to collect and process samples than to design the model structure,the proposed method is simpler and more feasible in implementation.This method has lower requirements on the running memory of the device and has more overall advantages.