In this post we’ll dive into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI at Superb AI. More specifically, how our data platform estimates the uncertainty of auto-labeled annotations and applies it to active learning.

1. A quick review of the efficacy of Auto-labeling

2. Method 1: Monte-Carlo Sampling

3. Method 2: Distribution Modeling

4. Evidential Deep Learning for Multi-Class Classification

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5. Superb AI’s Uncertainty Estimation

Ex. 1) Efficient Data Labeling and QA

Ex. 2) Efficient Model Training by Mining Hard Examples

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Coming Soon

About Superb AI

References