A Multi-Task Learning Model for Better Representation of Clothing Images

Abstract

Clothing images vary in style and everyone has a different understanding of style. Even with the current popular deep learning methods, it is difficult to accurately classify style labels. A style representation learning model based on the deep neural networks called StyleNet is proposed in this paper. We adopt a multi-task learning framework to build the model and make full use of various types of label information to represent the clothing images in a finer-grained manner. Due to the semantic abstraction of image labels in the current fashion field, using a simple migration learning method cannot fully meet the requirements of clothing image classification. An objective function optimization method is put forward by combining the distance confusion loss and the traditional cross entropy loss to improve the accuracy of StyleNet further. The experimental results show that by applying the multi-task representation learning framework, StyleNet can achieve a better classification accuracy, the optimized loss function can also bring performance improvement for deep learning models, and the classification effect of StyleNet becomes better as the size of the data set increases. In order to verify the robustness and effectiveness of the deep learning method in StyleNet, we also apply a Faster R-CNN module to pre-process the clothing images and use the result as the input of StyleNet. The classifier can only get a limited performance improvement, which is negligible compared with the methods proposed in this paper of increasing the depth of the neural network and optimizing the loss function.