{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:49:34Z","timestamp":1777567774218,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency components are actually much less exploited than the low- and mid- frequency components. We first investigate the frequency bias phenomenon by presenting two observations on feature discrimination and learning priority. Furthermore, we hypothesize that (1) the spectral density, (2) class consistency directly affect the frequency bias. Specifically, our investigations verify that the spectral density of datasets mainly affects the learning priority, while the class consistency mainly affects the feature discrimination.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/101","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"717-723","source":"Crossref","is-referenced-by-count":15,"title":["Investigating and Explaining the Frequency Bias in Image Classification"],"prefix":"10.24963","author":[{"given":"Zhiyu","family":"Lin","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jitao","family":"Sang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"},{"name":"Peng Cheng Lab, Shenzhen 518066, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:07:42Z","timestamp":1658142462000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/101"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/101","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}