{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:39:41Z","timestamp":1730302781035,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"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":[[2021,12,5]]},"DOI":"10.1109\/vcip53242.2021.9675360","type":"proceedings-article","created":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T20:37:19Z","timestamp":1642624639000},"page":"1-5","source":"Crossref","is-referenced-by-count":10,"title":["Learn to overfit better: finding the important parameters for learned image compression"],"prefix":"10.1109","author":[{"given":"Honglei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Francesco","family":"Cricri","sequence":"additional","affiliation":[]},{"given":"Hamed Rezazadegan","family":"Tavakoli","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Santamaria","sequence":"additional","affiliation":[]},{"given":"Yat-Hong","family":"Lam","sequence":"additional","affiliation":[]},{"given":"Miska M.","family":"Hannuksela","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2916167"},{"journal-title":"Out-of-distribution generalization via risk extrapolation (rex)","year":"2021","author":"krueger","key":"ref11"},{"key":"ref12","article-title":"Versatile Video Coding (Draft 8)","author":"bross","year":"2020","journal-title":"Joint Video Experts Team (JVET) Document JVET-L0165"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TCSVT.2015.2477916","article-title":"Video quality evaluation methodology and verification testing of HEVC compression performance","volume":"26","author":"tan","year":"2015","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"journal-title":"End-to-end Optimized Image Compression","year":"2017","author":"ball\u00e9","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01285"},{"journal-title":"ASNI\/IEEE Std 754–2019","article-title":"IEEE standards 754 for Floating-Point Arithmetic","year":"2019","key":"ref17"},{"key":"ref18","article-title":"NP-hardness of 10 minimization problems: revision and extension to the non-negative setting","author":"nguyen","year":"0","journal-title":"SampTA 2019 - 13th International Conference on Sampling Theory and Applications"},{"journal-title":"Statistical Learning with Sparsity The Lasso and Generalizations","year":"2019","author":"hastie","key":"ref19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP48831.2020.9287069"},{"key":"ref3","first-page":"9179","article-title":"A meta-analysis of overfitting in machine learning","volume":"32","author":"roelofs","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413536"},{"journal-title":"Compressing Weight-updates for Image Artifacts Removal Neural Networks","year":"2019","author":"lam","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69535-4_37"},{"journal-title":"Overfitting for Fun and Profit Instance-Adaptive Data Compression","year":"2021","author":"van rozendaal","key":"ref7"},{"key":"ref2","first-page":"352","article-title":"Stopped training and other remedies for overfitting","author":"sarle","year":"1996","journal-title":"Computing Science and Statistics"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00666"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01088"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"ref24","article-title":"EE1.2: Additional experimental results of NN-based super resolution (JVET-U0053)","author":"takeshi","year":"2021","journal-title":"document JVET-N1001 Joint Video Experts Team"},{"journal-title":"How transferable are features in deep neural networks?","year":"2014","author":"yosinski","key":"ref23"}],"event":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","start":{"date-parts":[[2021,12,5]]},"location":"Munich, Germany","end":{"date-parts":[[2021,12,8]]}},"container-title":["2021 International Conference on Visual Communications and Image Processing (VCIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9675283\/9675313\/09675360.pdf?arnumber=9675360","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:57:34Z","timestamp":1652201854000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9675360\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,5]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/vcip53242.2021.9675360","relation":{},"subject":[],"published":{"date-parts":[[2021,12,5]]}}}