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Why can't the process of "iterating on datasets" in the second half of the talk be automated? For example: - automatically learning that trolley is a not a gr
by zawerf 8y ago
Why can't the process of "iterating on datasets" in the second half of the talk be automated?
For example:
- automatically learning that trolley is a not a great fit for a "car" because they are behaviorally different
- reclassify that cluster as a new entity even if it doesn't know the english term "trolley".
- If it finds the new distinction useful, ping the human that it needs more training examples for that situation
Similar to how a human learner can identify that he's bad at something, figure out what the common problem is, and use that information to focus on what to practice on next.
I am sure he alluded to doing this in his talk but what's the technical term for it?
- nl 8y agoThis is what Active Training is. Basically you trace the boundaries in a classification model and provide more examples along those boundaries.
- zawerf 8y agoDoes this work even if the output format has to change? I can see that helping with class imbalance but not when a useful class is completely missing or in the wrong "format". For example in the bright smudges vs raindrops case, it might not have a label for the sun but it should be able to identify it as a important dimension in the cases it is getting wrong. Better yet, something more abstract like "smudge illuminated by light" or "bright background" that will be hard to annotate (e.g., how bright is bright?).
- nl 8y agoYes(ish). There are some practical software issues around not knowing the number of classes in advance, but those are "just coding". There is no reason why introducing a new class shouldn't be as simple as providing additional examples to an existing class.