Reproducibility Companion Paper
Reproducibility Companion Paper
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.
- Hong Kong Polytechnic University China (People's Republic of)
- Singapore Management University Singapore
- National Chiao Tung University Taiwan
- Shenzhen University China (People's Republic of)
- National University of Singapore Singapore
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence and Robotics, Time Series Forecasting, Theory and Algorithms, Fashion Analysis, Computer Science - Information Retrieval, Machine Learning (cs.LG), Multimedia (cs.MM), Fashion Trend Forecasting, Computer Science - Multimedia, Information Retrieval (cs.IR)
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence and Robotics, Time Series Forecasting, Theory and Algorithms, Fashion Analysis, Computer Science - Information Retrieval, Machine Learning (cs.LG), Multimedia (cs.MM), Fashion Trend Forecasting, Computer Science - Multimedia, Information Retrieval (cs.IR)
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