There are a lot of use-cases of facial recognition, mostly for security purposes like identifying criminals, searching for missing individuals, aid forensic investigations, etc. Intelligent marketing, diagnose diseases, track attendance in schools, are some other uses. It’s no secret that data is an increasingly important business asset, with the amount of data generated and stored globally growing at an exponential rate.
Companies like Zebra Medical Vision use semi-supervised learning for symptom detection in medical diagnostics. Models are trained based on a large amount of medical data to recognize typical patterns and identify deviations that may indicate the presence of a disease. But it doesn’t mean that semi-supervised learning is applicable to all tasks. If the portion of labeled data isn’t representative of the entire distribution, the approach may fall short. Say, you need to classify images of colored objects that have different looks from different angles. Unless you have a large amount of labeled data, the results will have poor accuracy.
