Clustering Without Knowing How To: Application and Evaluation

Autor:	Likhobaba, Daniil, Fedulov, Daniil, Ustalov, Dmitry
Rok vydání:	2022
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Information Retrieval I.5.3
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-28241-6_24
Popis:	Crowdsourcing allows running simple human intelligence tasks on a large crowd of workers, enabling solving problems for which it is difficult to formulate an algorithm or train a machine learning model in reasonable time. One of such problems is data clustering by an under-specified criterion that is simple for humans, but difficult for machines. In this demonstration paper, we build a crowdsourced system for image clustering and release its code under a free license at https://github.com/Toloka/crowdclustering. Our experiments on two different image datasets, dresses from Zalando's FEIDEGGER and shoes from the Toloka Shoes Dataset, confirm that one can yield meaningful clusters with no machine learning algorithms purely with crowdsourcing. Comment: accepted at ECIR 2023 Demonstration Track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.10267 Zobrazit plný text záznamu View this record from Arxiv