Deep Learning Classification System based on User Guidance

Currently, deep learning models are widely used in many applications. When utilizing these models to different environments, the experience like the distribution of objects in an environment can be used to further increase the accuracy of classification. In this paper, we carried out user guidance based deep learning classification task that is named as DLC-UG, which efficiently utilizes this kind distribution in an environment to increase the accuracy of classification. Firstly, we train the deep learning model on the training set. Secondly, the user can input the distribution of each label in corresponding environment by the experience of himself or advice from others. Thirdly, we can get the probability of labels on each sample by this trained model. Finally, the distribution information selectively cooperates with the probability of labels on testing samples. We select six popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can increase the accuracy of classification tasks, which is obviously higher than state-of-art methods.