Deep learning models have been used extensively to solve real-world problems in recent years. The performance of such models relies heavily on large amounts of labeled data for training. While the advances in data collection technology have enabled the acquisition of a massive volume of data, labeling the data remains an expensive and time-consuming task. Active learning techniques are being progressively adopted to accelerate the development of machine learning solutions by allowing the model to query the data they learn from.
In this presentation, Figure Eight’s Lead Machine Learning Scientist Humayun Irshad, introduces a real-world problem, the recognition of parking signs, and presents a framework that combines active learning techniques with a transfer learning approach and Human-in-the-Loop tools to create and train a machine learning solution to the problem.
- How and Active Learning framework contributes to building an accurate and cost-effective ML model
- Application of active learning approach to build an object detection system in an iterative manner
- How to select data for minority class from a pool of big dataset in an efficient way
- Taking publicly available data and using Humans-in-the-Loop to assist with labeling within an active learning framework to build a useful system