{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:20Z","timestamp":1761176120904,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>While its performance has been widely proven, the large deep learning model poses a severe computational burden and a potential overfitting problem when lacking sufficient training data. The current lightweight models and network pruning provide solutions by reducing redundant parameters; however, fundamentally reducing the redundant features during the training process is yet to be investigated. This paper proposes an innovative lightweight convolution WaveConv based on the wavelet transform. By separating high- and low-frequency features and spatially non-overlapping encoding, the WaveConv decomposes features into low and multi-scale high frequencies to reduce similar features in channel and space dimensions. We have evaluated our method on the MNIST, CamVid, and RFMiD datasets. Experiments demonstrate that WaveConv used 8% of the parameters of vanilla convolution to achieve comparable results on the Cifar-10 dataset. When being adapted to SOTA lightweight models of EfficientNet and MobileNetV3, WaveConv achieved a further 55% parameter reduction while retaining performance on RFMid and CamVid datasets.<\/jats:p>","DOI":"10.3233\/faia250816","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:57Z","timestamp":1761126177000},"source":"Crossref","is-referenced-by-count":0,"title":["WaveConv: Light-Weighted Wavelet Convolution"],"prefix":"10.3233","author":[{"given":"Jingwen","family":"Guan","sequence":"first","affiliation":[{"name":"The university of Sydney"}]},{"given":"Yichao","family":"Hao","sequence":"additional","affiliation":[{"name":"The university of Sydney"}]},{"given":"Bowen","family":"Xin","sequence":"additional","affiliation":[{"name":"CSIRO"}]},{"given":"Xiuying","family":"Wang","sequence":"additional","affiliation":[{"name":"The university of Sydney"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250816","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:57Z","timestamp":1761126177000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250816","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}