A E C B A E A E D A D C D C B A E B E 2 A A B B B B B A D E E E E E C D B B E B 3 A A B D D D C E A E C C C D E D B D D E C 4 A A B B D A A C D E E A C E E B A B D A D B D A ଟܾ ଟܾ͕ࣦഊ͢ΔྫʢશͯͰਖ਼ղ"ͱ͢Δʣ J. Li, Y. Baba and H. Kashima: Hyper Questions: Unsupervised Targeting of a Few Experts in Crowdsourcing, In CIKM, 2017.
ඃධՁऀͷೳྗΛਪఆ Y. Baba and H. Kashima: Statistical Quality Estimation for General Crowdsourcing Tasks, In KDD, 2013. T. Sunahase et al.: Pairwise HITS: Quality Estimation from Pairwise Comparisons in Creator-Evaluator Crowdsourcing Process, In AAAI, 2017.
ߟྀͨ͠ΞΠσΞબൈ J. Li, Y. Baba, and H. Kashima: Simultaneous clustering and ranking from pairwise comparisons, In IJCAI, 2018 Y. Baba, J. Li, and H. Kashima: CrowDEA: Multi-view Idea Prioritization with Crowds, In HCOMP, 2021.
Japan. Information Science and Technology, The University of Tokyo, Japan. ineering, Information and Systems, University of Tsukuba, Japan. CT enerative adversarial network ion as a discriminator. A ba- ent a real-data distribution by uishes real and generated data. esent the outside of a real-data ception, humans can recognize essed (i.e., a non-existent hu- human-acceptable distribution ne and cannot be modeled by an-acceptable distribution, we enerator training algorithm by ack-boxed discriminator. The training by using a computer . We evaluate our HumanGAN demonstrate that it can repre- Prior distr. Generated data Generator Discriminator Natu- ral Train to fool computer-based discriminator. GAN Training Distr. of training data Generation Distr. of human perception Fig. 1. Comparison of basic GAN and proposed HumanGAN. Basic GAN trains generator by fooling DNN-based discriminator (i.e., computer-based discriminator), and generator finally represents training-data distribution. In comparison, HumanGAN trains gener- ਓؒΛ᱐͢ੜثΛֶश͢Δ͜ͱͰ ਓؒͷײੑΛऔΓࠐΉ ྫɿϞωͱγεϨʔͷֆͷྨ ࣝผثͱͯ͠ਓؒΛ༻͍Δ K. Fujii, Y. Saito, S. Takamichi, Y. Baba, and H. Saruwatari: HumanGAN: Generative adversarial network with human-based discriminator and its evaluation in speech perception modeling. In ICASSP, 2020.
˙ ੜెͷशख़ʹ߹ΘͤͯదͳڭࡐΛఏࣔ͢Δ ᶈڭҭࢧԉ 9 T. Sunahase, Y. Baba and H. Kashima: Statistical modeling of peer correction and peer assessment, In EDM, 2019. Y. Mingzhe, and Y. Baba: Iterative machine teaching without teachers, arXiv:2006.15339. We observe that sometimes there is inconsistency between a grade and a correction; for example, a grader provides a high grade with a submission but she corrects many errors. We observed the occasional inconsistency between a grade and the correction; for example, a grader provides a high grade for a submission, but many errors were corrected. 80 点 ૬ޓఴ ఴલ ఴޙ ػցڭࣔ
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