{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:15:44Z","timestamp":1771953344415,"version":"3.50.1"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,23]]},"DOI":"10.1109\/icassp43922.2022.9746027","type":"proceedings-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T19:50:34Z","timestamp":1651089034000},"page":"3778-3782","source":"Crossref","is-referenced-by-count":2,"title":["Simpler is Better: Spectral Regularization and Up-Sampling Techniques for Variational Autoencoders"],"prefix":"10.1109","author":[{"given":"Sara","family":"Bjork","sequence":"first","affiliation":[{"name":"UiT The Arctic University of Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas Nordhaug","family":"Myhre","sequence":"additional","affiliation":[{"name":"NORCE Norwegian Research Centre"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Haugland Johansen","sequence":"additional","affiliation":[{"name":"NORCE Norwegian Research Centre"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1117\/12.152694"},{"key":"ref31","article-title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","volume":"33","author":"tancik","year":"2020","journal-title":"NeurIPS"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00236"},{"key":"ref37","article-title":"Implicit Rank-Minimizing Autoencoder","volume":"33","author":"jing","year":"2020","journal-title":"NeurIPS"},{"key":"ref36","first-page":"3e","article-title":"Deconvolution and Checkerboard Artifacts","volume":"1","author":"odena","year":"2016","journal-title":"UML Distilled"},{"key":"ref35","article-title":"A guide to convolution arithmetic for deep learning","author":"dumoulin","year":"2016","journal-title":"arXiv preprint arXiv 1603 07660"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1561\/9781680836233"},{"key":"ref10","first-page":"5767","article-title":"Improved Training of Wasserstein GANs","volume":"30","author":"gulrajani","year":"2017","journal-title":"NeurIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref12","article-title":"Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images","author":"child","year":"2021","journal-title":"Proc of ICLR"},{"key":"ref13","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"lindbo larsen","year":"2016","journal-title":"Proc of ICML"},{"key":"ref14","first-page":"6307","article-title":"Neural Discrete Representation Learning","volume":"2017 decem","author":"van den oord","year":"2017","journal-title":"NeurIPS"},{"key":"ref15","article-title":"Generating Diverse High-Fidelity Images with VQ-VAE-2","volume":"32","author":"razavi","year":"2019","journal-title":"NeurIPS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref17","first-page":"5301","article-title":"On the Spectral Bias of Neural Networks","author":"rahaman","year":"2019","journal-title":"Proc of ICML"},{"key":"ref18","article-title":"Spatial Frequency Bias in Convolutional Generative Adversarial Networks","author":"khayatkhoei","year":"2020","journal-title":"arXiv preprint arXiv 2010 00170"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00712"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3100891"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1609\/aaai.v35i2.16196","article-title":"SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains","author":"chen","year":"2021","journal-title":"Proc Of AAAI"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01366"},{"key":"ref5","first-page":"19667","article-title":"NVAE: A Deep Hierarchical Variational Autoencoder","author":"vahdat","year":"2020","journal-title":"NeurIPS"},{"key":"ref8","first-page":"2223","article-title":"Un-paired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks","author":"zhu","year":"2017","journal-title":"Proc of IEEE ICCV"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"ref2","article-title":"Auto-Encoding Variational Bayes","author":"kingma","year":"2014","journal-title":"Proc of ICLR"},{"key":"ref9","article-title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","author":"radford","year":"2015","journal-title":"Proc of ICLR"},{"key":"ref1","first-page":"2672","article-title":"Generative Adversarial Nets","author":"goodfellow","year":"2014","journal-title":"NeurIPS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"ref22","first-page":"3205","article-title":"Leveraging Frequency Analysis for Deep Fake Image Recognition","volume":"partf16814","author":"frank","year":"2020","journal-title":"Proc of ICML"},{"key":"ref21","article-title":"Implicit Neural Representations with Periodic Activation Functions","volume":"33","author":"sitzmann","year":"2020","journal-title":"NeurIPS"},{"key":"ref24","article-title":"Fourier Spectrum Discrepancies in Deep Network Generated Images","volume":"33","author":"dzanic","year":"2020","journal-title":"NeurIPS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"ref26","article-title":"A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model","volume":"33","author":"czolbe","year":"2020","journal-title":"NeurIPS"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1609\/aaai.v35i2.16267","article-title":"Spectral Distribution Aware Image Generation","volume":"35","author":"jung","year":"2021","journal-title":"Proc Of AAAI"}],"event":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Singapore, Singapore","start":{"date-parts":[[2022,5,23]]},"end":{"date-parts":[[2022,5,27]]}},"container-title":["ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9745891\/9746004\/09746027.pdf?arnumber=9746027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T19:14:50Z","timestamp":1700507690000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9746027\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/icassp43922.2022.9746027","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}