The success of CNNs in predicting the response of real visual neurons (and similar findings for audition) suggest they are going in the right direction wrt getting the logic right. But yea just cuz you hook some artificial neurons up doesnt mean they'll always work like the brain
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Replying to @neurograce @chophshiy and
agreed with
@neurograce, e only additional point I am making here is that we ought have some humility here, 33 years after White et al's 1986 landmark worm connectome, which in honesty we must recognize we have not yet fully understood. read it, and weep: https://royalsocietypublishing.org/doi/abs/10.1098/rstb.1986.0056 …1 reply 0 retweets 2 likes -
Replying to @GaryMarcus @neurograce and
@neurograce has written this excellent article on the DNNs + visual system comparisons:https://neurdiness.wordpress.com/2018/05/17/deep-convolutional-neural-networks-as-models-of-the-visual-system-qa/ …2 replies 3 retweets 10 likes -
Replying to @kateiyas @neurograce and
this is a good QA but the query about what visual system has that CNNs lack also include compositionality, ability to recognize line drawings, silhouettes, and other abstractions even without explicit training data, ability to infer relationships between objects, etc
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Replying to @GaryMarcus @kateiyas and
Sure it could include those too, which people are also working on. I wrote it before the bulk of the work, eg, showing that CNNs respond more to texture than shape, but that's something people have already found ways of countering.
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Replying to @neurograce @GaryMarcus and
And to be clear, a standard AlexNet is not *catastrophic* on silhouettes or line drawings, it isnt as good as humans (chance here is 1/16) and this paper introduces training that can help with that https://arxiv.org/abs/1811.12231 pic.twitter.com/zRHHTiKuYX
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Replying to @neurograce @GaryMarcus and
One of my final year undergrads looked at something similar to this. Interestingly, he did edge detection with the other colour scheme (white lines on black) and found that almost all the big models mistook line drawings for spider webs or fountains for what seem obvious reasonspic.twitter.com/ofjGkI0LOC
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Replying to @neuralreckoning @GaryMarcus and
It's a particularly nice demo, but basically not a new point. Was already well understood that most of these models are dominated by texture over shape.
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it’s exactly the sort of thing that should be added to #10 in @neurograce’s otherwise excellent FAQ.
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Replying to @GaryMarcus @neuralreckoning and
Itll be in the upcoming review!
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Replying to @neurograce @GaryMarcus and
Interestingly, most ancient hardwired recognition systems in our brain (visual neurons of the pulvinar) also rely on texture and not shape. ANNs are just a few millennia behind, that's all.
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End of conversation
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