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Same problem about biased algorithms is discussed in this book:Algorithms of Oppression
Book by Safiya Noble.
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So, if face training with pictures of only one face, how to confirm it won’t get confused when tested with different faces? Wouldn’t it need to be trained by finding the difference between faces?
It couldn’t be trained solely on one person’s face. It would have to be a dataset that contains many pictures of the person you’re trying to identify and many pictures of other random people. My point was just that you don’t have to show it every other person in order to build a classifier that works.
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Is domain shift, same as concept drift?
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I’ll plug my project which is looking at “out of domain” situation with different chess piece sets (as noted by @wdhorton) if you have any thought
In the example of data shift where the classifier goes from bears to raccoons should you do transfer learning from your previous model or retrain completely?
thanks!
If the difference is big like bears vs racoons and you already have a good pretrained model (like the ones we use at the base of our training), I would say retrain.
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One interesting example of domain shift that’s present in astrophysics is when we train models on simulated data, e.g., a simulated Universe of galaxies, but then we want to do inference on observed data from an actual telescope, where you end up with correlated noise, weird systematic effects, and physical effects that are quite difficult to simulate.
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Very interesting. What has been done in order to solve this problem?
Jeremy, does fast.ai have methods built in that provide for incremental learning?
(i.e. improving the model slowly over time with a single data point each time?)
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How do you take care of these domain shifts ?
Lots of folks are still working on it 
But if you have any cool ideas, I’d love to chat!
EDIT: I should mention that there have been lots of smart people, mostly in the deep learning field, who are working on domain adaptation. For example, generative modeling along with clever loss functions can help with transferring knowledge across domains! So that would be a great place for us to start.
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No, you will have to implement it using the data loading API in fastai.
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In the manual process, if we are looking at slides of pathological tissue, would you recommend the viewers to be blinded?
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for that same example of predicting images from video, how would you recommend running that on a server? not necessarily a constant video stream but if users were uploading like 5 second videos
Nice. Datasheets for datasets really helps you review how you collect the data. It took me 3 weeks to make one. 
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What is the problem you are referring to? Like a classification problem? We would want the pathologists to manually confirm the predictions of the model, so it might not be blinded.