{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:09:59Z","timestamp":1740100199802,"version":"3.37.3"},"reference-count":36,"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"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency (DARPA)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533449","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Assisting the Adversary to Improve GAN Training"],"prefix":"10.1109","author":[{"given":"Andreas","family":"Munk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Harvey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Wood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Smoothness and stability in gans","author":"chu","year":"0","journal-title":"Eighth International Conference on Learning Representations"},{"key":"ref32","article-title":"Large scale gan training for high fidelity natural image synthesis","author":"brock","year":"2019","journal-title":"ArXiv"},{"key":"ref31","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"ulyanov","year":"2017","journal-title":"ArXiv"},{"key":"ref30","article-title":"Geometric gan","author":"lim","year":"2017","journal-title":"ArXiv"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref35","article-title":"Began: Boundary equilibrium generative adversarial networks","author":"berthelot","year":"2017","journal-title":"ArXiv"},{"key":"ref34","first-page":"7354","article-title":"Self-attention generative adversarial networks","volume":"97","author":"zhang","year":"2019","journal-title":"ser Proceedings of Machine Learning Research"},{"key":"ref10","first-page":"3581","article-title":"A large-scale study on regularization and normalization in gans","author":"kurach","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref11","article-title":"Nips 2016 tutorial: Generative adversarial networks","author":"goodfellow","year":"2017","journal-title":"ArXiv"},{"key":"ref12","article-title":"On convergence and stability of gans","author":"kodali","year":"2017","journal-title":"ArXiv"},{"key":"ref13","first-page":"3481","article-title":"Which training methods for gans do actually converge?","author":"mescheder","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref14","first-page":"2203","article-title":"Mmd gan: Towards deeper understanding of moment matching network","volume":"30","author":"li","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref15","first-page":"2018","article-title":"Stabilizing training of generative adversarial networks through regularization","volume":"30","author":"roth","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref16","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref17","first-page":"1825","article-title":"The numerics of gans","volume":"30","author":"mescheder","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref18","first-page":"5585","article-title":"Gradient descent gan optimization is locally stable","volume":"30","author":"nagarajan","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref19","first-page":"5767","article-title":"Improved training of wasserstein gans","volume":"30","author":"gulrajani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00296"},{"key":"ref4","first-page":"3449","article-title":"Hype: A benchmark for human eye perceptual evaluation of generative models","author":"zhou","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref27","article-title":"An online learning approach to generative adversarial networks","author":"grnarova","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref3","first-page":"271","article-title":"F-gan: Training generative neural samplers using variational divergence minimization","volume":"29","author":"nowozin","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref6","first-page":"1530","article-title":"Variational inference with normalizing flows","author":"rezende","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref29","volume":"48","author":"bertsekas","year":"1997","journal-title":"Nonlinear Programming ser Journal of the Operational Research Society"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"ref8","first-page":"14866","article-title":"Generating diverse high-fidelity images with vq-vae-2","author":"razavi","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref7","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2014","journal-title":"ArXiv"},{"key":"ref2","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref9","article-title":"Nvae: A deep hierarchical variational autoen-coder","author":"vahdat","year":"2020","journal-title":"ArXiv"},{"key":"ref1","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref20","first-page":"8110","article-title":"An-alyzing and improving the image quality of stylegan","author":"karras","year":"0","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00332"},{"key":"ref24","article-title":"Learning Multiple Layers of Features from Tiny Images","author":"krizhevsky","year":"2009","journal-title":"CiteSeer"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref26","first-page":"2234","article-title":"Improved techniques for training gans","volume":"29","author":"salimans","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref25","first-page":"6626","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"heusel","year":"2017","journal-title":"Advances in neural information processing systems"}],"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\/09533449.pdf?arnumber=9533449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:11Z","timestamp":1652197571000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533449\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533449","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}