Abstract
This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged into other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Computer Vision (ICCV) |
Editors | L. O'Conner |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 5296-5305 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-5386-1032-9 |
ISBN (Print) | 978-1-5386-1033-6 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 IEEE International Conference on Computer Vision (ICCV) - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017 |
Conference
Conference | 2017 IEEE International Conference on Computer Vision (ICCV) |
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Abbreviated title | ICCV 2017 |
Country/Territory | Italy |
City | Venice |
Period | 22/10/17 → 29/10/17 |
Keywords
- Decoding
- Gallium nitride
- Computational modeling
- Standards
- Visualization
- Learning systems
- Predictive models