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Title | JP Lewis - |
Description | JP Lewis • Research Publications Notes and Codes Images Overview Current research interests include applications of statistical learning and computer visi |
Keywords | N/A |
WebSite | scribblethink.org |
Host IP | 66.33.221.205 |
Location | United States |
Site | Rank |
US$2,399,304
Last updated: 2023-05-10 20:15:31
scribblethink.org has Semrush global rank of 4,411,407. scribblethink.org has an estimated worth of US$ 2,399,304, based on its estimated Ads revenue. scribblethink.org receives approximately 276,843 unique visitors each day. Its web server is located in United States, with IP address 66.33.221.205. According to SiteAdvisor, scribblethink.org is safe to visit. |
Purchase/Sale Value | US$2,399,304 |
Daily Ads Revenue | US$2,215 |
Monthly Ads Revenue | US$66,443 |
Yearly Ads Revenue | US$797,307 |
Daily Unique Visitors | 18,457 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
scribblethink.org. | A | 7199 | IP: 66.33.221.205 |
scribblethink.org. | NS | 14400 | NS Record: ns2.dreamhost.com. |
scribblethink.org. | NS | 14400 | NS Record: ns3.dreamhost.com. |
scribblethink.org. | NS | 14400 | NS Record: ns1.dreamhost.com. |
JP Lewis • Research Publications Notes and Codes Images Overview Current research interests include applications of statistical learning and computer vision in the arts. Projects Neural matte extraction without training: The motivation for training a deep learning model is the assumption that it can be trained once and used many times, as in the case of video conferencing with many calls each involving mostly front-facing humans. Visual effects often violates this assumption, as it requires matte extraction from diverse environments and props (underwater, crashed space ship model) many of which will only be seen a few times, and with no easy way to produce training data. This work introduces a training-free approach to neural matte extraction, using the deep image prior to trade computation for data. Deep Learning without Back-Propagation: This paper (arxiv 2019, AAAI 20) is the first work to demonstrate successful deep net training exclusively using the information bottleneck |
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