H11-M08_WEIGHT.zip
From the dataset abstract
Method 最先進的（SOTA）方法通常是通過監督式學習來訓練的，這需要大量的labeled data。由於標記數資料需要大量的人力和時間成本，尤其是那些需要由專家來標記的資料(如醫學相關)，所需要的成本更是難以負擔。Unlabeled data因為不需要標註所以取得相對容易且成本較低，因此如何能有效利用unlabeled...
Source: H11-M08_漸進式資料標註更正的自主學習模型
Additional Information
| Field | Value |
|---|---|
| Data last updated | October 5, 2023 |
| Metadata last updated | October 5, 2023 |
| Created | October 5, 2023 |
| Format | ZIP |
| License | Other (Non-Commercial) |
| Created | over 1 year ago |
| Media type | application/zip |
| Size | 993,768,623 |
| format | ZIP |
| has views | True |
| id | 99d6f3bc-6ae2-4166-9bba-d2490df005ca |
| last modified | over 1 year ago |
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| on same domain | True |
| package id | 6461a428-dcdb-432e-bb23-11024aaf1024 |
| position | 2 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/6461a428-dcdb-432e-bb23-11024aaf1024/resource/99d6f3bc-6ae2-4166-9bba-d2490df005ca/nchcproxy/H11-M08_WEIGHT.zip |
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| state | active |
| url type | upload |
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