2024
Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization
Liu X, Marin T, Eslahi S, Tiss A, Chemli Y, Johson K, Fakhri G, Ouyang J. Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. 2011 IEEE Nuclear Science Symposium Conference Record 2024, 00: 1-1. PMID: 39445307, PMCID: PMC11497478, DOI: 10.1109/nss/mic/rtsd57108.2024.10656150.Peer-Reviewed Original ResearchDomain generalizationDenoising performanceDenoising moduleDeep learningSubject-independent mannerSubject-invariant featuresSuperior denoising performanceAdversarial learning frameworkSubject-related informationConventional UNetBottleneck featuresTrustworthy systemsLearning frameworkDL modelsDL model performanceDenoisingNoise realizationsNegative samplesList-mode dataImage volumesModel performancePerformancePerformance of positron emission tomographyUNetFraction of events
2022
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
Zhuang C, Xiang V, Bai Y, Jia X, Turk-Browne N, Norman K, DiCarlo J, Yamins D. How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? Advances In Neural Information Processing Systems 2022, 35: 22628-22642. PMID: 38435074, PMCID: PMC10906807.Peer-Reviewed Original ResearchSelf-supervised algorithmLearning algorithmsReal-timeStreams of visual inputNeural network modelHuman learning abilitiesMoCo v2Catastrophic forgettingLearning benchmarksLearning capabilityVisual inputReal worldHuman learnersNetwork modelVisual knowledgeLeverage memoryPerformance of modelsAlgorithmHuman performanceBenchmarksNegative samplesContext-sensitiveLearning abilityLearningVision setting
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