Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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