Advanced ML Methods For Automating Image Labeling
Training computer vision models require a constant feed of large and accurately labeled datasets. However, this typically requires large time and capital commitments, especially since most of the labeling and quality assurance is done manually by humans. Can most, if not all, of this workflow be automated intelligently? Join Superb AI's CEO, Hyun Kim, as he talks about how Superb AI uses advanced ML techniques like transfer and few-shot learning to help teams automate the labeling and auditing of computer vision datasets.
Key takeaways :
Data labeling is a big bottleneck for teams, in both time and cost.
Labeling automation isn't of much value on it's own if auditing is not intelligently automated as well
Even with automation, teams need to craft precise and repeatable workflows around data preparation and data-ops
Hyunsoo (Hyun) Kim, co-founder and CEO of Superb AI, is an entrepreneur on a mission to democratize data and artificial intelligence. With a background in Deep Learning and Robotics during his Ph.D. studies at Duke University and career as a Machine Learning Engineer, Hyun saw the need for a more efficient way for companies to handle machine learning training data.
Hyun has also been selected as the featured honoree for the Enterprise Technology category of Forbes 30 Under 30 Asia 2020, and Superb AI graduated from Y Combinator, a prominent Silicon Valley startup accelerator.