TARGET OPTIMIZATION DIRECTION GUIDED TRANSFER LEARNING FOR IMAGE CLASSIFICATION

At present, deep learning has made impressive achievements in various fields; however, effectively training deep neural networks on small data sets remains a significant challenge. Transfer learning, as a method of efficient training across multiple tasks, has been widely used to solve this problem. However, when the domain gap or the data volume difference between the two tasks is too large, the transfer learning may not perform well, and other optimization methods will be required to improve the performance. In this paper, we propose a new transfer learning method guided by the direction of objective optimization from the perspective of gradient. This method guides the gradient direction of the source task towards the gradient direction of the target task. In several similar and conflicting tasks, this method has achieved good results in efficiency and performance. In comparison with other transfer learning methods, the results shown by this method are generally better.