Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2022Embargo end date: 01 Aug 2022Publisher:ZenodoJournal:CoRR, volume abs/2208.09284

Authors: Sen, Roopsa; Sinha, Sidharth; Maheshwari, Parv; Jha, Animesh; Chakravarty, Debashish;

doi: 10.48550/arxiv.2208.09284 , 10.5281/zenodo.6574697 , 10.5281/zenodo.6574696

arXiv: 2208.09284

Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations

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Abstract

The following paper is a reproducibility report for "Social NCE: Contrastive Learning of Socially-aware Motion Representations" {\cite{liu2020snce}} published in ICCV 2021 as part of the ML Reproducibility Challenge 2021. The original code was made available by the author \footnote{\href{https://github.com/vita-epfl/social-nce}{https://github.com/vita-epfl/social-nce}}. We attempted to verify the results claimed by the authors and reimplemented their code in PyTorch Lightning.

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, deep learning, Machine Learning (cs.LG), python, machine learning, rescience c, pytorch

Impact byBIP!

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