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by tasuki14 hours ago |
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upvoteby jrflo13 hours ago|
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A CNN is also definitely not an “outdated architecture” when it comes to image classification
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upvoteby koutakun9 hours ago|
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They have sort of been replaced by transformers for a lot of use cases (and where dataset size allows).
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upvoteby mcv4 hours ago|
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Are transformers better at all use cases? Or only a specific subset? This sounds like a classification problem to me, which neural networks have always been pretty good at.
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upvoteby grumbelbart21 hours ago|
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It's mostly a scale thing. Transformers scale better when you have a lot of training data. (Not saying CNNs don't scale, but it's way more difficult to tune the hyper parameters).

For smaller training data sizes, transformers don't have a clear advantage over CNNs. CNNs are still the way to go if you have problems that are not too complex, and especially constraints regarding inference costs. Think of a small edge device / sensor that counts apples in your apple picking machine.

What we sometimes see is that a backbone is trained using transformers, then a smaller CNN is destilled from that (DinoV3 for example).

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upvoteby darkslide30002 hours ago|
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In other news, a 10 year old outdated box of matches is better at starting a fire than a 2026 state-of-the-art high-power flashlight.
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upvoteby sungam14 hours ago|
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I think the reason I am surprised is that they are exceptionally good at interpreting images generally and there are tens of thousands of labeled skin cancer images on the public internet so I had assumed the general image interpretation abilities would transfer
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upvoteby ang_cire9 hours ago|
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Having actually worked with teams doing medical vision models for cancer detection, I can tell you that the datasets we used were not public, and they came from studies and private hospital partnerships that LLM companies don't have access to.
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upvoteby jrflo12 hours ago|
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The specialized medical diagnosis models are trained on that same data though, and also use neural networks. The more noteworthy story will be when they overtake specialized models.
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upvoteby gradascent9 hours ago|
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Not only do they train on that data but they train ONLY on that data and don’t have to generalize to other domains. And the architectures themselves, including hyperparameters, loss function, etc are chosen and tuned specifically for the quirks of this domain. So this result doesn’t surprise me either.
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upvoteby ip2611 hours ago|
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such as helping you create a network that's good at playing go

This is really what’s happening right now… LLMs are excellent computer users. Which makes one of the frontiers “amazing things computers always could have done, but we lacked the programmers to do”.

If basically anyone can train a high quality CNN for just about any purpose now, what new opportunities appear that computers can help with?

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