Timestamps: 00:00: Introducing Omar and Phillip 01:12: How to Get Value Out of Synthetic Data 02:06: Agenda 02:54: Problems with Current ML Development Practices 04:23: Edge Case Detection using Synthetic Data 05:11: Parallel Domain’s Image Data (parking, trailers, debris, road signs, etc.) 05:45: Parallel Domain’s Sensors and Annotation types (labels) 06:19: Performance Improvements using Synthetic Data 07:45: Iterative Synthetic Dataset Design 09:15: Domain Gap Ontology 25:29 Parallel Domain SDK 28:46 Demo - using Synthetic Data for 2D Object Detection 32:11 Demo - Performance Iteration with Synthetic Data 35:11 Dataset Design - Optical Flow Example 39:54 Dataset Design - Semantic Segmentation UDA Example 43:43 Demo - Reducing Synthetic Data Iteration Times using Statistics 47:41 Why Parallel Domain 48:44 Parallel Domain at CVPR 51:24 Questions and Answers
Timestamps:
00:00: Introducing Omar and Phillip
01:12: How to Get Value Out of Synthetic Data
02:06: Agenda
02:54: Problems with Current ML Development Practices
04:23: Edge Case Detection using Synthetic Data
05:11: Parallel Domain’s Image Data (parking, trailers, debris, road signs, etc.)
05:45: Parallel Domain’s Sensors and Annotation types (labels)
06:19: Performance Improvements using Synthetic Data
07:45: Iterative Synthetic Dataset Design
09:15: Domain Gap Ontology
25:29 Parallel Domain SDK
28:46 Demo - using Synthetic Data for 2D Object Detection
32:11 Demo - Performance Iteration with Synthetic Data
35:11 Dataset Design - Optical Flow Example
39:54 Dataset Design - Semantic Segmentation UDA Example
43:43 Demo - Reducing Synthetic Data Iteration Times using Statistics
47:41 Why Parallel Domain
48:44 Parallel Domain at CVPR
51:24 Questions and Answers