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[D] research papers related to tesla autonomy day’s “data engine”

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

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

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

submitted by /u/CartPole
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.