{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:27:23Z","timestamp":1730266043466,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533462","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T17:27:41Z","timestamp":1632158861000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Jitter: Random Jittering Loss Function"],"prefix":"10.1109","author":[{"given":"Zhicheng","family":"Cai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidan","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1038\/nbt1206-1565"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1903070116"},{"key":"ref33","article-title":"Selfnorm and crossnorm for out-of-distribution robustness","author":"tang","year":"2021","journal-title":"ArXiv e-prints"},{"journal-title":"Batch group normalization","year":"2020","author":"zhou","key":"ref32"},{"journal-title":"Autodropout Learning dropout patterns to regularize deep networks","year":"2021","author":"pham","key":"ref31"},{"key":"ref30","first-page":"10727","article-title":"Dropblock: A regularization method for convolutional networks","volume":"31","author":"ghiasi","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref37","first-page":"950","article-title":"A simple weight decay can improve generalization","volume":"4","author":"krogh","year":"1991","journal-title":"Advances in neural information processing systems"},{"journal-title":"xgboost Extreme gradient boosting","year":"2016","author":"chen","key":"ref36"},{"key":"ref35","first-page":"1401","article-title":"A brief introduction to boosting","volume":"99","author":"schapire","year":"1999","journal-title":"IJCAI"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00490.x"},{"key":"ref40","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"ref12","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref15","article-title":"The effectiveness of data augmentation in image classification using deep learning","author":"perez","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref17","first-page":"630","article-title":"Generalization and parameter estimation in feedforward nets: Some experiments","volume":"2","author":"morgan","year":"1989","journal-title":"Advances in neural information processing systems"},{"key":"ref18","article-title":"Do we need zero training loss after achieving zero training error?","author":"ishida","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref19","article-title":"Deep double descent: Where bigger models and more data hurt","author":"nakkiran","year":"2019","journal-title":"ArXiv Preprint"},{"journal-title":"Delving deep into label smoothing","year":"2020","author":"zhang","key":"ref28"},{"key":"ref4","first-page":"402","article-title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping","author":"caruana","year":"2001","journal-title":"Advances in neural information processing systems"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00065"},{"key":"ref3","article-title":"Cross-validation for detecting and preventing overfitting","author":"moore","year":"2001","journal-title":"School of Computer Science Carneigie Mellon University"},{"key":"ref6","first-page":"9179","article-title":"A meta-analysis of overfitting in machine learning","author":"roelofs","year":"2019","journal-title":"Advances in neural information processing systems"},{"journal-title":"Towards understanding label smoothing","year":"2020","author":"xu","key":"ref29"},{"key":"ref5","first-page":"2300","article-title":"Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate","author":"belkin","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"ref7","volume":"328","author":"tikhonov","year":"2013","journal-title":"Numerical Methods for the Solution of Ill-Posed Problems"},{"key":"ref2","first-page":"245","article-title":"Preventing“ overfitting” of cross-validation data","volume":"97","author":"ng","year":"1997","journal-title":"ICML"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref1","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref20","first-page":"270","article-title":"On linear problems which are not well-posed","volume":"145","author":"ivanov","year":"1962","journal-title":"Doklady Akademii Nauk"},{"key":"ref22","first-page":"49","article-title":"On the regularization of ill-posed problems","volume":"153","author":"tikhonov","year":"1963","journal-title":"Doklady Akademii Nauk"},{"key":"ref21","first-page":"1035","article-title":"Solution of incorrectly formaulated problems and the regularization method","volume":"151","author":"tikhonov","year":"1963","journal-title":"Dokl Akad Nauk"},{"journal-title":"Adversarial autoaugment","year":"2019","author":"zhang","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2200\/S00822ED1V01Y201712COV015"},{"journal-title":"Data augmentation for object detection via differentiable neural rendering","year":"2021","author":"ning","key":"ref26"},{"journal-title":"Data augmentation revisited Rethinking the distribution gap between clean and augmented data","year":"2019","author":"he","key":"ref25"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533462.pdf?arnumber=9533462","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:45:57Z","timestamp":1652183157000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533462\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533462","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}