Name
Abstract | ||
|---|---|---|
Deep learning has emerged as a powerful tool for feature-driven labeling of datasets. However, for it to be effective, it requires a large and finely labeled training dataset. Precisely labeling a large training dataset is expensive, time-consuming, and error prone. In this article, we present a visually driven deep-learning approach that starts with a coarsely labeled training dataset and iteratively refines the labeling through intuitive interactions that leverage the latent structures of the dataset. Our approach can be used to (a) alleviate the burden of intensive manual labeling that captures the fine nuances in a high-dimensional dataset by simple visual interactions, (b) replace a complicated (and therefore difficult to design) labeling algorithm by a simpler (but coarse) labeling algorithm supplemented by user interaction to refine the labeling, or (c) use low-dimensional features (such as the RGB colors) for coarse labeling and turn to higher-dimensional latent structures that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets and a user study.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3150977 | TiiS |
Keywords | Field | DocType |
Deep learning, dimensionality reduction, knowledge discovery, semantic interaction, visual interaction | Dimensionality reduction,Use case,Pattern recognition,Visual interaction,Computer science,Knowledge extraction,RGB color model,Artificial intelligence,Deep learning,Distributed computing | Journal |
Volume | Issue | ISSN |
9 | 1 | 2160-6455 |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Krokos | 1 | 9 | 1.95 |
| Hsueh-Chien Cheng | 2 | 15 | 2.52 |
| Jessica Chang | 3 | 5 | 1.46 |
| Bohdan A. Nebesh | 4 | 0 | 0.68 |
| Celeste Lyn Paul | 5 | 4 | 2.41 |
| Kirsten Whitley | 6 | 11 | 1.44 |
| Amitabh Varshney | 7 | 1704 | 172.25 |