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SLEAP: A deep learning system for multi-animal pose tracking
Talmo D. Pereira
, Nathaniel Tabris
, Arie Matsliah
, David M. Turner
, Junyu Li
, Shruthi Ravindranath
, Eleni S. Papadoyannis
, Edna Normand
,
David S. Deutsch
, Z. Yan Wang
, Grace C. McKenzie-Smith
, Catalin C. Mitelut
, Marielisa Diez Castro
, John D’Uva
, Mikhail Kislin
, Dan H. Sanes
, Sarah D. Kocher
, Samuel S.H. Wang
, Annegret L. Falkner
, Joshua W. Shaevitz
Mala Murthy
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Research output
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Dive into the research topics of 'SLEAP: A deep learning system for multi-animal pose tracking'. Together they form a unique fingerprint.
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Keyphrases
Deep Learning System
100%
Pose Tracking
100%
Animal Pose
100%
Natural Environment
12%
Social Animals
12%
Computer Vision
12%
Latency
12%
Social Behavior
12%
Social Interaction
12%
Behavioral Patterns
12%
Unseen
12%
Individual Animal
12%
Deep Learning
12%
Animal Behavior
12%
Data Standardization
12%
Methodological Innovation
12%
Machine Learning System
12%
Gerbil
12%
Image Resolution
12%
Model Inference
12%
Parts Grouping
12%
Model Driven Architecture
12%
Time Application
12%
Data Labeling
12%
Graphical User Interface
12%
Markerless Pose Estimation
12%
Identity Tracking
12%
Frames per Second
12%
Configuration System
12%
Unique Challenge
12%
Model Training
12%
Systematically Evaluate
12%
Pose Feature
12%
Computer Science
Learning System
100%
Deep Learning Method
100%
Data Model
50%
Pose Estimation
50%
Model Architecture
50%
Configuration System
50%
Real-Time Application
50%
Graphical User Interface
50%
Machine Learning
50%
Computer Vision
50%
Behavior Pattern
50%