Domain Adaptation for Visual Understanding
This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applica...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Domain Adaptation for Visual Understanding
- M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
- Improving Transferability of Deep Neural Networks
- Cross Modality Video Segment Retrieval with Ensemble Learning
- On Minimum Discrepancy Estimation for Deep Domain Adaptation
- Multi-Modal Conditional Feature Enhancement for Facial Action Unit Recognition
- Intuition Learning
- Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating