Deep Interactive Learning with Sharkzor
Abstract
Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. After interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1402064
- Resource Type:
- Multimedia
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; SHARKZOR; MACHINE-LEARNING; ALGORITHMS; IMAGES; TRIAGE; ORGANIZE; AUTOMATE; N-SHOT LEARNING
Citation Formats
None. Deep Interactive Learning with Sharkzor. United States: N. p., 2017.
Web.
None. Deep Interactive Learning with Sharkzor. United States.
None. Mon .
"Deep Interactive Learning with Sharkzor". United States. https://www.osti.gov/servlets/purl/1402064.
@article{osti_1402064,
title = {Deep Interactive Learning with Sharkzor},
author = {None},
abstractNote = {Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. After interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {10}
}