A knowledge-sharing semi-supervised approach for fashion clothes classification and attribute prediction

• Published in: The Visual computer Vol. 38; no. 11; pp. 3551 - 3561
• Main Authors: Shajini, Majuran, Ramanan, Amirthalingam
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Classification; Computer Graphics; Computer Science; Datasets; Deep learning; Entropy; Fashion models; Image Processing and Computer Vision; Knowledge sharing; Learning; Machine learning; Neural networks; Original Article; Semantics; Semi-supervised learning; Teachers; DeepFashion; Fashion attribute prediction; Teacher–student pair model; Fashion clothes classification; Semi-supervised learning
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-021-02178-3

Fashion is a form of self-expression that permits us to manifest our personality and identity with more confidence. Visual fashion clothing analysis has attracted researchers who have sought to pioneer deep learning concepts. In this work, we introduce a semi-supervised multi-task learning approach intending to attain clothing category classification and attribute prediction. For intensifying semi-supervised fashion clothes analysis, we embrace a teacher–student (T–S) pair model that utilises weighted loss minimisation while sharing knowledge between teacher and student. Our focus in this work is on strengthening the feature representation by simultaneous learning of labelled and unlabelled samples that avoids additional training for unlabelled samples. As a result, our approach involves in gaining beneficiary performance by making use of semi-supervised learning in fashion clothing analysis. We evaluated the proposed approach on the large-scale DeepFashion-C dataset and the combined unlabelled dataset obtained from six publicly available datasets. Experimental results show that the proposed paired architecture involving deep neural networks is comparable to state-of-the-art techniques in fashion clothing analysis.