{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:41:54Z","timestamp":1775695314721,"version":"3.50.1"},"reference-count":14,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"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":[[2019,6]]},"DOI":"10.1109\/newcas44328.2019.8961246","type":"proceedings-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T12:48:04Z","timestamp":1579610884000},"page":"1-4","source":"Crossref","is-referenced-by-count":2,"title":["Using deep learning approaches to overcome limited dataset issues within semiconductor domain"],"prefix":"10.1109","author":[{"given":"Milad Omrani","family":"Tamrin","sequence":"first","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastien","family":"Henwood","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Fran\u00e7ois","family":"Dubois","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Jules","family":"Brault","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saad","family":"Chidami","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel-Jean","family":"Bassetto","sequence":"additional","affiliation":[{"name":"Polytechnique Montreal,Montréal,QC,CANADA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","author":"huang","year":"2018","journal-title":"IntroVAE Introspective Variational Autoencoders for Photographic Image Synthesis"},{"key":"ref11","author":"maalee","year":"2019","journal-title":"BIVA A Very Deep Hierarchy of Latent Variables for Generative Modeling"},{"key":"ref12","author":"kingma","year":"2014","journal-title":"Adam A method for stochastic optimization"},{"key":"ref13","article-title":"Progressive growing of gan for improved quality, stability, and variation","volume":"abs 1710 10196","author":"karras","year":"2017","journal-title":"CoRR"},{"key":"ref14","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"2017","journal-title":"NIPS-W"},{"key":"ref4","first-page":"2487","author":"chen","year":"2016","journal-title":"Dcan Deep contour-aware networks for accurate gland segmentation"},{"key":"ref3","first-page":"2672","author":"goodfellow","year":"2014","journal-title":"Generative adversarial nets"},{"key":"ref6","first-page":"661","article-title":"Does the wake-sleep algorithm produce good density estimators?","author":"frey","year":"1996","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref8","author":"kingma","year":"2013","journal-title":"Auto-encoding variational bayes"},{"key":"ref7","first-page":"2172","article-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref2","author":"soenjaya","year":"2005","journal-title":"MINING WAFER FABRICATION FRAMEWORK AND CHALLENGES"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mee.2004.12.003"},{"key":"ref9","author":"oord","year":"2017","journal-title":"Neural Discrete Representation Learning"}],"event":{"name":"2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)","location":"Munich, Germany","start":{"date-parts":[[2019,6,23]]},"end":{"date-parts":[[2019,6,26]]}},"container-title":["2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8955776\/8961212\/08961246.pdf?arnumber=8961246","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T18:07:45Z","timestamp":1757095665000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8961246\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1109\/newcas44328.2019.8961246","relation":{},"subject":[],"published":{"date-parts":[[2019,6]]}}}