I’m a grad student interested in the process described by Andrej at tesla’s autonomy day. Below I’ve put together some of my brief notes with links. So far the published research I could find that seemed to be related to their work was the NeurIPS 2017 paper Decoupling “when to update” from “how to update” and the Active Learning Survey

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Does anyone else have other related papers to suggest? For example, I’m guessing measuring distance via the L2 is a bad idea.

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Generic Object Detection improvement

If you know a specific problem you have: take that specific problem and use it to find similar examples to pull into a training set

I’m guessing they embed every image with a generic imagenet model and then find similar images based on L2 distance between embedded vectors

training pipeline

start training with a uniformly sampled dataset and select new images for training if:

1. detect uncertainties in the network predictions
   1. I’m guessing 2 networks disagreeing with each other(similar to: decoupling what to update from how to update)
2. driver intervention

Too fix either of (1) or (2) use the process described in generic object detection