target sample
Reinforced Cross-Domain Knowledge Distillation on Time Series Data
D istillation ( RCD-KD) which can effectively adapt to student's network capability via dynamically selecting suitable target domain samples for knowledge transferring.
Genre:
- Research Report > Experimental Study (0.93)
- Research Report > New Finding (0.67)
Industry:
- Education (1.00)
- Information Technology (0.67)
Technology:
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Qiming ZHANG, Jing Zhang, Wei Liu, Dacheng Tao
Unsupervised domain adaptation (UDA) aims to enhance the generalization capability ofacertain model from asource domain toatargetdomain.
Country:
- Oceania > Australia (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Asia > Middle East > Jordan (0.04)
- Asia > China (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.46)
Technology:
Country:
- North America > Canada (0.04)
- Asia > Singapore (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
Country:
- Asia > Middle East > Jordan (0.04)
- Asia > China (0.04)
Technology:
Country:
- Asia > Middle East > Jordan (0.04)
- North America > Canada (0.04)
Country:
- Asia > Middle East > Jordan (0.04)
- Asia > India (0.04)
Technology:
AdaptingNeuralArchitecturesBetweenDomains
Neural architecture search (NAS) has demonstrated impressive performance in automatically designing high-performance neural networks. The power ofdeep neural networks is to be unleashed for analyzing a large volume of data (e.g.
Country:
- Asia > China (0.04)
- Oceania > Australia (0.04)
- North America > Canada > Ontario > Toronto (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
