[HN Gopher] A Cookbook of Self-Supervised Learning ___________________________________________________________________ A Cookbook of Self-Supervised Learning Author : ZunarJ5 Score : 79 points Date : 2023-04-25 16:13 UTC (6 hours ago) (HTM) web link (arxiv.org) (TXT) w3m dump (arxiv.org) | cs702 wrote: | Nice. It looks like it will be useful... although best practices | are likely going to continue to evolve. The biggest question in | my mind is whether we can come up, at some point in the future, | with a kind of universal self-supervised learning objective that | works well in practice for any task. | nmaley wrote: | The NFL theorem means nothing if all the learning tasks have a | common underlying structure. In the real world, they do. The | laws of physics and chemistry create emergent causal | relationships. Any SSL learning algorithm that learns to | exploit causal relationships will consistently perform well | over a variety of real world tasks. | medo-bear wrote: | no such thing as free lunch - | https://en.m.wikipedia.org/wiki/No_free_lunch_theorem | tomrod wrote: | We really need to revise this to say "there is no global free | lunch." | | You can and often do get local free lunches. | medo-bear wrote: | the question was about "objective that works well in | practice for any task". thats pretty global in my books | karpierz wrote: | If you take "any task" to mean literally any conceivable | task, then sure. | | If you take "any task" to mean "any practical task" or | "any task a human would conceivably want to have done", | then no free lunch doesn't apply. | tbalsam wrote: | Another way of looking at karpierz's comment is through | the incompressibility of pure noise at scale. | | As soon as some infinitely generated sort of noise is | from some subset of possible noise, there is indeed | (AFAIK) some kind of an ideal estimator that | appropriately compresses that noise source with no bias | and less entropy that the full space of possible noise. | | I hope this shines an additional alternative light on the | topic. | tbalsam wrote: | The most common statement I make is about the misapplication | of the NFL. | | This is not an appropriate use of the NFL. | | The original commenter is asking about a general solution | that will work well for all problems presented at it. The NFL | details fine-grained tradeoffs in _ideal solutions_ for | specific traits in certain areas. Additionally, we're not | operating in an unbiased space here, so trivially there is a | best estimator without bias. So by that very fact alone the | NFL is invalidated in terms of the method it is being applied | in here. | | This is something I feel frustrated seeing a lot of younger | people entering the field do (not saying you are young or | new, it's just the trend). If this was the case in this kind | of a way we never would have gotten beyond MLPs. | | Yes, indeed there is in fact a set of general solutions that | works roughly well over everything and is biased towards the | situations where people will need it the most. | | No, it will likely not be the technically best performing | solution. | | What the OP is looking for I believe is convenience, | stability, and reliably good performance. | | Hence, the NFL is not applicable or relevant to this matter | in the way it is being used. ___________________________________________________________________ (page generated 2023-04-25 23:00 UTC)