{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:57:55Z","timestamp":1761897475953,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687625"},{"type":"electronic","value":"9783030687632"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68763-2_46","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:28:24Z","timestamp":1613838504000},"page":"605-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras"],"prefix":"10.1007","author":[{"given":"Francesco","family":"Versaci","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"46_CR1","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265\u2013283 (2016)"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Addison, P.S.: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. CRC Press, Boca Raton (2017)","DOI":"10.1201\/9781315372556"},{"issue":"1","key":"46_CR3","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","volume":"38","author":"HU Amin","year":"2015","unstructured":"Amin, H.U., et al.: Feature extraction and classification for eeg signals using wavelet transform and machine learning techniques. Australas. Phys. Eng. Sci. Med. 38(1), 139\u2013149 (2015)","journal-title":"Australas. Phys. Eng. Sci. Med."},{"issue":"8","key":"46_CR4","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"J Bruna","year":"2013","unstructured":"Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872\u20131886 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"46_CR5","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/MM.2020.2971677","volume":"40","author":"J Burgess","year":"2020","unstructured":"Burgess, J.: Rtx on-the nvidia turing gpu. IEEE Micro 40(2), 36\u201344 (2020)","journal-title":"IEEE Micro"},{"key":"46_CR6","unstructured":"Chollet, F., et al.: Keras: the python deep learning library. Astrophysics Source Code Library (2018)"},{"issue":"7","key":"46_CR7","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1002\/cpa.3160410705","volume":"41","author":"I Daubechies","year":"1988","unstructured":"Daubechies, I.: Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41(7), 909\u2013996 (1988)","journal-title":"Commun. Pure Appl. Math."},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Daubechies, I.: Ten Lectures on Wavelets, vol. 61. Siam, Thailand (1992)","DOI":"10.1137\/1.9781611970104"},{"key":"46_CR9","unstructured":"Fujieda, S., Takayama, K., Hachisuka, T.: Wavelet convolutional neural networks for texture classification (2017)"},{"key":"46_CR10","unstructured":"Haug, K.M.: Stability of Adaptive Neural Networks for Image Reconstruction. Master\u2019s thesis (2019)"},{"key":"46_CR11","unstructured":"Howard, J.: Fastai\u2019s imagenette and imagewoof datasets (2020). https:\/\/github.com\/fastai\/imagenette"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Huang, H., He, R., Sun, Z., Tan, T.: Wavelet-srnet: a wavelet-based cnn for multi-scale face super resolution. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.187"},{"issue":"3","key":"46_CR13","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/MM.2018.032271057","volume":"38","author":"N Jouppi","year":"2018","unstructured":"Jouppi, N., Young, C., Patil, N., Patterson, D.: Motivation for and evaluation of the first tensor processing unit. IEEE Micro 38(3), 10\u201319 (2018)","journal-title":"IEEE Micro"},{"issue":"36","key":"46_CR14","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.21105\/joss.01237","volume":"4","author":"G Lee","year":"2019","unstructured":"Lee, G., Gommers, R., Waselewski, F., Wohlfahrt, K., O\u2019Leary, A.: Pywavelets: a python package for wavelet analysis. J. Open Source Softw. 4(36), 1237 (2019)","journal-title":"J. Open Source Softw."},{"key":"46_CR15","doi-asserted-by":"publisher","first-page":"74973","DOI":"10.1109\/ACCESS.2019.2921451","volume":"7","author":"P Liu","year":"2019","unstructured":"Liu, P., Zhang, H., Lian, W., Zuo, W.: Multi-level wavelet convolutional neural networks. IEEE Access 7, 74973\u201374985 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"46_CR16","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TSG.2013.2260421","volume":"5","author":"H Livani","year":"2013","unstructured":"Livani, H., Evrenosoglu, C.Y.: A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Trans. Smart Grid 5(1), 51\u201359 (2013)","journal-title":"IEEE Trans. Smart Grid"},{"key":"46_CR17","unstructured":"Lohne, M.: Parseval Reconstruction Networks. Master\u2019s thesis (2019)"},{"issue":"7","key":"46_CR18","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674\u2013693 (1989)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"46_CR19","unstructured":"Oliveira, B.: pytest Quick Start Guide: Write Better Python Code with Simple and Maintainable Tests. Packt Publishing Ltd., Birmingham (2018)"},{"key":"46_CR20","unstructured":"Paleo, P.: pypwt, parallel discrete wavelet transform (2020). https:\/\/github.com\/pierrepaleo\/pypwt"},{"key":"46_CR21","unstructured":"Rodriguez, M.X.B., et al.: Deep adaptive wavelet network. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 3111\u20133119 (2020)"},{"issue":"3","key":"46_CR22","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"46_CR23","unstructured":"Walt, S.V.D., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22\u201330 (2011)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68763-2_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T17:39:12Z","timestamp":1613842752000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68763-2_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687625","9783030687632"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68763-2_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}