Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting

TL;DR

This paper provides a reproducibility artifact for the KERN method in fashion trend forecasting, enabling easy replication and validation of the original experiments' results.

Contribution

It offers a Python-based artifact that facilitates the replication of the KERN model experiments, supporting transparency and validation in fashion trend forecasting research.

Findings

Replication confirms original results

Supports main claims of the original paper

Provides an easy-to-use implementation artifact

Abstract

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.