Task-to-Task Transfer Learning with Parameter-Efficient Adapter

2020 ◽  
pp. 391-402
Author(s):  
Haiou Zhang ◽  
Hanjun Zhao ◽  
Chunhua Liu ◽  
Dong Yu
Keyword(s):  
Transfer Learning ◽  
Task Transfer

A multi-task transfer learning method with dictionary learning

2020 ◽  
Vol 191 ◽  
pp. 105233 ◽  
Author(s):  
Xin Zheng ◽  
Luyue Lin ◽  
Bo Liu ◽  
Yanshan Xiao ◽  
Xiaoming Xiong
Keyword(s):  
Transfer Learning ◽  
Dictionary Learning ◽  
Learning Method ◽  
Task Transfer

scholarly journals 2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20245-20256 ◽  
Author(s):  
Junying Gan ◽  
Li Xiang ◽  
Yikui Zhai ◽  
Chaoyun Mai ◽  
Guohui He ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Facial Beauty ◽  
Task Transfer

scholarly journals Probabilistic Policy Reuse for inter-task transfer learning

2010 ◽  
Vol 58 (7) ◽  
pp. 866-871 ◽  
Author(s):  
Fernando Fernández ◽  
Javier García ◽  
Manuela Veloso
Keyword(s):  
Transfer Learning ◽  
Task Transfer

scholarly journals Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuanci Zheng ◽  
Jie Li ◽  
Hongfei Ji ◽  
Lili Duan ◽  
Maozhen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Training Model ◽  
Average Accuracy ◽  
Source Data ◽  
Task Transfer ◽  
Calibration Time ◽  
And Training ◽  
Bci System

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.

Multi-task transfer learning for biomedical machine reading comprehension

2020 ◽  
Vol 23 (3) ◽  
pp. 234
Author(s):  
Yiliang Zhao ◽  
Keyan Ren ◽  
Wenyang Guo ◽  
Yongping Du
Keyword(s):  
Reading Comprehension ◽  
Transfer Learning ◽  
Task Transfer ◽  
Machine Reading

Multiple task transfer learning with small sample sizes

2015 ◽  
Vol 46 (2) ◽  
pp. 315-342 ◽  
Author(s):  
Budhaditya Saha ◽  
Sunil Gupta ◽  
Dinh Phung ◽  
Svetha Venkatesh
Keyword(s):  
Transfer Learning ◽  
Small Sample ◽  
Sample Sizes ◽  
Task Transfer ◽  
Small Sample Sizes ◽  
Multiple Task

scholarly journals Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?

2020 ◽  
Author(s):  
Yada Pruksachatkun ◽  
Jason Phang ◽  
Haokun Liu ◽  
Phu Mon Htut ◽  
Xiaoyi Zhang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Language Models ◽  
Task Transfer
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