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 <\/p>\n

\u7814\u7a76\u30c6\u30fc\u30de<\/strong><\/span><\/p>\n

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\n\u2022 \u8996\u899a\u7cfb\u306e\u6700\u5f8c\u306e\u30b9\u30c6\u30fc\u30b8\u3067\u3042\u308b\u9ad8\u6b21\u8996\u899a\u91ce\u3067\u306f\u3001\u9854\u306e\u5165\u529b\u306b\u7279\u306b\u3088\u304f\u53cd\u5fdc\u3059\u308b\u30cb\u30e5\u30fc\u30ed\u30f3\u7fa4\u304b\u3089\u306a\u308b\u300c\u9854\u8a8d\u8b58\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u300d\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u8a08\u7b97\u65b9\u5f0f\u3092\u63a2\u308b\u305f\u3081\u3001\u300c\u6df7\u5408\u30b9\u30d1\u30fc\u30b9\u7b26\u53f7\u5316\u30e2\u30c7\u30eb\u300d\u3092\u63d0\u6848\u3057\u3001\u30de\u30ab\u30af\u30b6\u30eb\u306e\u9854\u9818\u91ce\u306b\u95a2\u3059\u308b\u5b9f\u9a13\u4e8b\u5b9f\u3092\u8aac\u660e\u3059\u308b\u3053\u3068\u306b\u6210\u529f\u3057\u307e\u3057\u305f [3] \uff08\u56f3A)\u3002
\n\u2022 \u4eba\u5de5\u77e5\u80fd\u5206\u91ce\u3067\u6709\u540d\u306a\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u306f\u3001\u8fd1\u5e74\u306e\u8996\u899a\u7cfb\u306e\u8a08\u7b97\u8ad6\u3067\u3082\u53d6\u308a\u4e0a\u3052\u3089\u308c\u308b\u3053\u3068\u3082\u591a\u3044\u3067\u3059\u3002\u3057\u304b\u3057\u3001\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3067\u306f\u9854\u8a8d\u8b58\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u7279\u6027\u3092\u3059\u3079\u3066\u306f\u8aac\u660e\u3067\u304d\u306a\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3057\u305f [4]\uff08\u56f3B\uff09\u3002<\/p>\n

\u73fe\u5728\u3001\u3053\u308c\u3089\u306e\u7406\u8ad6\u304b\u3089\u51fa\u305f\u4eee\u8aac\u3092\u3001\u5b9f\u9a13\u7684\u306b\u691c\u8a3c\u3059\u308b\u7814\u7a76\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3092\u3001\u795e\u7d4c\u751f\u7406\u5b66\u306e\u5c02\u9580\u5bb6\u305f\u3061\u3068\u5171\u540c\u3067\u9032\u3081\u3066\u3044\u307e\u3059\u3002<\/p>\n

[1] Hosoya H, Hyv\u00e4rinen A. A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2. Journal of Neuroscience. 2015;35:10412\u201328.
\n[2] Haruo Hosoya, Aapo Hyv\u00e4rinen. Learning Visual Spatial Pooling by Strong PCA Dimension Reduction. Neural Computation, 82:1-16, 2016.
\n[3] Haruo Hosoya, Aapo Hyv\u00e4rinen. A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing. PLoS Computational Biology, 13(7): e1005667, 2017.
\n[4] Raman, R., Hosoya, H. Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex. Communications Biology, volume 3, Article number: 221 (2020).<\/p>\n

\"\u7d30\u8c37\u753b\u50cf\uff11\"<\/a><\/p>\n

 <\/p>\n

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[1] Hosoya, H. Group-based learning of disentangled representations with generalizability for novel contents. The International Joint Conference on Artificial Intelligence (IJCAI) , Aug, 2019.<\/p>\n

\"\u7d30\u8c37\u753b\u50cf\uff12\"<\/a><\/p>\n

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\n\u2022 \u7d30\u8c37\u3000\u6674\u592b
\n\u2022 Rajani Raman (KU Leuven)
\n\u2022 Aapo Hyv\u00e4rinen (U Helsinki)
\n\u2022 Winrich Freiwald (Rockefeller U)<\/p>\n","protected":false},"excerpt":{"rendered":"

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