| |
 |
| Paper: |
Prototype of Interactive Visualisation Tool for Bayesian Active Deep Learning |
| Volume: |
535, Astronomical Data Analysis Software and SystemsXXXI |
| Page: |
91 |
| Authors: |
Podsztavek, O.; Skoda, P.; Tvrdik, P. |
| Abstract: |
In the era of big data in astronomy, we need to develop methods to analyse the data. One such method is Bayesian active deep learning (synergy of Bayesian convolutional neural networks and active learning). To improve the method’s performance, we have developed a prototype of an interactive visualisation tool for a selection of an informative (contains data with high predictive uncertainty, is diverse, but not redundant) data subsample for labelling by a human expert. The tool takes as input a sample of data with the highest predictive uncertainty. These data are projected to 2-D with a dimensionality reduction technique. We visualise the projected data in an interactive scatter plot and allow a human expert to label a selected subsample of data. With this tool, she or he can select a correct subsample with all the previously mentioned characteristics. This should lower the total amount of data labelled because the Bayesian model’s performance will improve faster than when the data are selected automatically. |
|
|
|
|
|
