Multi-task learning with Attention : Constructing auxiliary tasks for learning to learn

With the development of deep learning in various fields, a deep learning method that can optimize multiple target tasks at the same time, that is, deep multi-task learning (MTL), has attracted more and more attention. We aim to find a multi-task learning method to optimize a single goal. For image classification tasks, it is difficult to find multiple tasks with task relevance. We use an unsupervised clustering algorithm to construct multiple related auxiliary tasks in the dataset to solve this problem in order to achieve a kind of data enhancement. The purpose is to improve the accuracy of the main task. While these newly constructed auxiliary tasks may exhibit semantic features that are not relevant to the main task, in order to reduce the impact of such non-ideal auxiliary tasks on the main task, we use a multi-task learning based on learning to learn (MTL-LTL) approach with a spatially dependent attention function embedded in an underlying joint model with hard parameter sharing that allows our model to have pixelated modeling capabilities. In addition, we also use the method of learning to learn to randomly sample multiple auxiliary tasks. It can train these tasks on the shared hidden layer, and at the same time minimize the loss of the main task, and ensure that the optimization direction leads to the improvement of the main task. In order to verify the classification performance of the model, three image dataset are used for experimental analysis. The experimental results show that the model proposed in this paper is better than the current popular benchmark methods and can effectively improve the accuracy of image classification.