Good points! Most data is def crap lol. I don't see few shot, one-shot or no-shot getting anywhere close to standard supervised learning for anything practica

Good points! Most data is def crap lol. I don't see few shot, one-shot or no-shot getting anywhere close to standard supervised learning for anything practical. It really doesn't make sense in production settings at all. You have a function that you want to learn, let's say mapping between an RGB image to a segmentation map. For most applications you're never really in a situation where a production product is dealing with visual scenes/objects it has never seen before. In a factory, in smart stores, in cars, AR scenarios like I just don't see it happening. And then if this case is removed, I'm thinking, ok so when can I get good enough results from a tiny dataset? Machine Learning isn't magic, you're trying to learn a function with 100 million parameters using a dataset, I just don't see the math working out. More data provides better results, it inserts more information to create a more relevant mapping function from input to output. Third point is great! As long as the models are somehow based on real-world scans I think a lot of good can come from this. The funny thing is, there is so much bias in networks trained today precisely because the data captured is usually small and captured from a specific area/population/setting. If you had a great synthetic data generation engine you could at least generate equal representation of gender, age groups, ethnicities, ... etc. Overall great points!