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Comment by nickodell | Hacker News Reader

>In perception you hit false positives/negatives all the time but appearance of the same issue on two-three consecutive frames goes to 0 pretty quickly.

If the frames are all very similar, wouldn't the probability of failure be correlated? In other words, if I feed a picture of a panda to a NN, and it incorrectly classifies it, why would it be correct the second time around?

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Sure, but I bet Andrej Karpathy's team is using SOTA and mitigates most of the issues like the ones you mentioned. OK it's an "argument from authority" one might believe or not and I doubt they'd ever disclose things that make it work in their specific case. But realistically, if you e.g. observe results of real-time semantic segmentation, you see that surfaces flip a bit in each frame but mostly stay correct; you can e.g. use average IoU/coverage from past n frames to estimate what is going on. They have a fleet of cars driving around receiving more training data in all kinds of environments to handle various perceptual conditions as well.

>Sure, but I bet Andrej Karpathy's team is using SOTA and mitigates most of the issues like the ones you mentioned.

Can you give an example of an image classification technique where classification failures are independent, or independent enough?

Time-distributed 3D convolutions/attention-enhanced 2D CNNs, i.e. you look at a sequence of images at once, not at a single image.

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