{"componentChunkName":"component---src-templates-blog-post-js","path":"/blog/learning-to-learn-by-yourself-unsupervised-meta-learning-with-self-knowledge-distillation-for-covid-19-diagnosis-from-pneumonia-cases/","result":{"data":{"site":{"siteMetadata":{"title":"No Frills News"}},"contentfulNfnPost":{"postTitle":"Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases","slug":"learning-to-learn-by-yourself-unsupervised-meta-learning-with-self-knowledge-distillation-for-covid-19-diagnosis-from-pneumonia-cases","createdLocal":"2021-05-14 14:32:44.883524","publishDate":"None","feedName":"Image Recognition","sourceUrl":{"sourceUrl":"https://onlinelibrary.wiley.com/doi/10.1002/int.22449"},"postSummary":{"childMarkdownRemark":{"html":"<p>To the best of our knowledge, our proposed data set is the largest data set compared to existing publicly available COVID‐19 data sets except for normal cases .\nIt is worth noting that the proposed data set is an unbalanced data set, which is exactly consistent with the long‐tail problem solved by meta‐learning.\nIn this section, we first describe how we built our proposed COVID‐19 Pneumonia Data set and introduce the structure of our proposed data set.\nThen, we can get the anchor images set X a , positive images set X p and negative images set X n by following the above strategy.\nOn the COVID‐CT data set, we follow the data split of this data set.</p>"}}}},"pageContext":{"slug":"learning-to-learn-by-yourself-unsupervised-meta-learning-with-self-knowledge-distillation-for-covid-19-diagnosis-from-pneumonia-cases"}}}