Active Learning, Data Selection, Data Auto-Labeling, and Simulation in Autonomous Driving — Part 2

Isaac Kargar
5 min readFeb 14, 2024

Now, let’s look at what big companies do in the real world.

NVIDIA

NVIDIA proposes to use pool-based active learning and an acquisition function based on a disagreement between several trained models (the core of their system is an ensemble of object detectors providing potential bounding boxes and probabilities for each class of interest) to select the frames which are most informative to the model. Here are the steps in their proposed…

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Isaac Kargar

Co-Founder and CIO @ Resoniks | Ph.D. candidate at the Intelligent Robotics Group at Aalto University | https://kargarisaac.github.io/