Synthetic Data: The Early Days, Part II
We continue from last time, when we began a...
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
Together, we’re building the future of computer vision & machine learning
We continue from last time, when we began a discussion of the origins and first applications of synthetic data: using simple artificial drawings for specific problems and using synthetically generated datasets to compare different computer vision algorithms. Today, we will learn how people made self-driving cars in the 1980s and see that as soon as computer vision started tackling real world problems with machine learning, it could not avoid synthetic data.
Previously on this blog, we have discussed the data problem: why machine learning may be hitting a wall, how one-shot and zero-shot learning can help, how come reinforcement learning does not need data at all, and how unlabeled datasets can inform even supervised learning tasks. Today, we begin discussing our main topic: synthetic data. Let us start from the very beginning: how synthetic data was done in the early days of computer vision…