Thông tin tài liệu
Thông tin siêu dữ liệu biểu ghi
Trường DC | Giá trị | Ngôn ngữ |
---|---|---|
dc.contributor.author | Philip, Kenneweg | - |
dc.contributor.author | Dominik, Stallmann | - |
dc.contributor.author | Barbara, Hammer | - |
dc.date.accessioned | 2023-03-30T08:10:15Z | - |
dc.date.available | 2023-03-30T08:10:15Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s00521-022-08115-2 | - |
dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7341 | - |
dc.description | CC BY | vi |
dc.description.abstract | Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where suitable deep network models are readily available. In this contribution, we address an important application area in the domain of biotechnology, the automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation, where data characteristics are very dissimilar to existing domains and trained deep networks cannot easily be adapted by classical transfer learning. | vi |
dc.language.iso | en | vi |
dc.publisher | Springer | vi |
dc.subject | Transfer learning schemes | vi |
dc.subject | CHO-K1 suspension growth | vi |
dc.title | Novel transfer learning schemes based on Siamese networks and synthetic data | vi |
dc.type | Book | vi |
Bộ sưu tập | OER - Công nghệ thông tin |
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