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| http://dx.doi.org/10.1186/s40695-022-00082-x | DOI Listing |
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Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled source domain and unlabeled target domain with mode match. However, CADA provides wrong multimodal information for challenging target features due to utilizing classifier predictions as the multimodal information, leading to distribution mismatch and less robust domain-invariant features.
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The scientific literature on talent identification is extensive, with significant advancements made over the past 30 years. However, as with any field, the translation of research into practice and its impact on the field have been slower than anticipated. Indeed, recent findings highlight a pervasive relative age effect, the effects of maturation being often overlooked, disparate populations between young and senior performers, and a necessity to embrace a holistic approach.
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View Article and Find Full Text PDFAssociation between dietary fructose and human colon DNA methylation: implication for racial disparities in colorectal cancer risk using a cross-sectional study.
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