For the first time we quantified the advantages of semi-supervised labeling based on the public Tsinghua-Tencent 100K benchmark in the dimensions of costs, quantity, quality and duration. Check out how humans compare to today’s AI and find out what are their typical errors.
Semi-supervised labeling will be a prerequisite to efficiently build rich, high quality datasets in the order of million of unique training samples and hundreds of classes. But next to the significant cost saving potential in the current domains it will enable AI to enter completely new domains in the industry.
Download the full benchmark results