Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

• Main Authors: MA, Yunshan, DING, Yujuan, YANG, Xun, LIAO, Lizi, WONG, Wai Keung, CHUA, Tat-Seng, MOON, Jinyoung, SHUAI, Hong-Han
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Fashion Trend Forecasting; Fashion Analysis; Time Series Forecasting; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7097; https://ink.library.smu.edu.sg/context/sis_research/article/8100/viewcontent/3460426.3463598_pvoa.pdf
• Institution: Singapore Management University

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.