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Question to deep learning practitioners: Is there any research on comparing the 99.9% confidence about something wrong, to the human weakness of 'optical illus

by fizixer 11y ago

Question to deep learning practitioners: Is there any research on comparing the 99.9% confidence about something wrong, to the human weakness of 'optical illusion'? If you watch a few minutes of this Dan Ariely TED Talk [1], we humans are fooled into near certainty that the table on the left has longer vertical distance that the horizontal distance of that on the right. Even after the speaker shows this is not the case, we still "confidently see" the wrong thing. (not to mention, we suffer from non-optical illusions as well, like cognitive biases, and fallacious reasoning, as shown later in the talk). It seems to me you guys have discovered 'machine optical illusion'. It's just that the machine has illusions about something completely different. I believe exploring the space of architectures/weights/what-not might lead towards making a machine get into the same kind of optical illusions as the human visual system gets into. At the least, it looks like a very promising research direction to me. [1] https://youtu.be/9X68dm92HVI?t=2m22s https://youtu.be/9X68dm92HVI?t=2m22s

rasz_pl 11y ago
length illusion happens higher up the meat stack. You see the picture for what it is, but visual cortex gets overridden. Happens all the time, blue dress, motion blur, saccades. A ton of stuff seen by the eye is filtered out or overruled by upper layers.
brianchu 11y ago
That is not an example of how weak our brain is. That is an example of how incredibly intelligent our brain is. If you saw both tables in the real, physical, natural world, because of the effect of perspective, the table on the left is indeed physically longer than the width of the table on the right. Our brain is correctly inferring this from perspective/trigonometry. The visual system is designed to correctly interpret the natural world, not correctly interpret lengths of line segments on a slide. This illustrates how hard it is to create computer algorithms that robustly infer 3D from 2D (this inference requires taking cues from shading, perspective, etc.) Almost all optical illusions are examples of the strength, not weakness, of the human brain. EDIT: I've emphasized the word "natural". The problem with the below examples of "illusions" constructed in the physical world is this: what do you expect the brain to do otherwise? When you see a scene in the world, is it a weakness that our brain discards the (extremely unlikely) possibility the scene is just a massive hologram five inches from your eyes, or a meticulously painted and lit cardboard scene? I don't.
- jessriedel 11y ago
  Many (probably most) of these optical illusions can be translated to the real physical world. Not surprising, it just requires situations that are outside the normal experiences of the human brain, especially in the ancestral environment. For instance: https://s-media-cache-ak0.pinimg.com/736x/24/5b/59/245b59444cdcff14d139df7c37b7e271.jpg https://s-media-cache-ak0.pinimg.com/736x/24/5b/59/245b59444...
  - im2w1l 11y ago
    I like this one http://0.media.collegehumor.cvcdn.com/70/36/483778211d5177e9b7b74dbc8bbc8290-amazing-floating-paper-cube-illusion.gif http://0.media.collegehumor.cvcdn.com/70/36/483778211d5177e9... It seems impossible a few seconds until the trick is revealed.
    - dr_zoidberg 11y ago
      I was expecting that (or the T-rex variation of) when he mentioned optical illusions. The image he posted is more related to perspective than how the visual cortex works -- that is, it can even be done inside a 3d game/engine, with clever texturing.
- zardeh 11y ago
  This would be a textbook example of overfitting, yes?
  - IshKebab 11y ago
    Yeah I suppose you could say that our "training set" doesn't include optical illusions. Though I wouldn't really call it overfitting because our brains haven't learnt specific training inputs, which is what happens with overfitting.
nullc 11y ago
Search for explaining and harnessing adversarial examples for some contemplation on the overly confident results.
jkldotio 11y ago
I find it less interesting in terms of applications. I am sure finding the causes of these illusions will help in understanding aspects of how the system, human or machine, subject to the illusion operates; however, so many of them depend on either a static image or a fixed perspective that it just doesn't seem to be all that big of an issue to me. The visual illusions humans are subject to are frequently defeated by some simple motion to reposition the viewer and there seem to be few illusions that can sustain being in motion themselves and still fool people. From that it doesn't seem all that revolutionary that a fixed position view of a three dimensional reality, with no fourth dimension of time, is not omniscient. It only has one bias sample or view of the scene, so why would we expect it to be omniscient? The paper "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"[0] is interesting in its explorations of the issue but I still don't get the hype around "fooling" DNNs. Even if someone gets an actual video scene, a timeseries of frame after frame, that still fools some kind of DNN (perhaps a LSTM => softmax classification), it's still not all that interesting as that occasionally seems to happen to humans too. [0]http://arxiv.org/abs/1412.1897 http://arxiv.org/abs/1412.1897 (web, but the pdf linked from there is ~9.5MB)
amelius 11y ago
I'd say that the problem is not well-defined. You are asking to create a 3d reconstruction from 2*2d image data, so no wonder that some information has to be made up.

by fizixer 11y ago

rasz_pl 11y ago

length illusion happens higher up the meat stack. You see the picture for what it is, but visual cortex gets overridden. Happens all the time, blue dress, motion blur, saccades. A ton of stuff seen by the eye is filtered out or overruled by upper layers.

brianchu 11y ago

That is not an example of how weak our brain is. That is an example of how incredibly intelligent our brain is. If you saw both tables in the real, physical, natural world, because of the effect of perspective, the table on the left is indeed physically longer than the width of the table on the right. Our brain is correctly inferring this from perspective/trigonometry. The visual system is designed to correctly interpret the natural world, not correctly interpret lengths of line segments on a slide. This illustrates how hard it is to create computer algorithms that robustly infer 3D from 2D (this inference requires taking cues from shading, perspective, etc.) Almost all optical illusions are examples of the strength, not weakness, of the human brain. EDIT: I've emphasized the word "natural". The problem with the below examples of "illusions" constructed in the physical world is this: what do you expect the brain to do otherwise? When you see a scene in the world, is it a weakness that our brain discards the (extremely unlikely) possibility the scene is just a massive hologram five inches from your eyes, or a meticulously painted and lit cardboard scene? I don't.

jessriedel 11y ago
Many (probably most) of these optical illusions can be translated to the real physical world. Not surprising, it just requires situations that are outside the normal experiences of the human brain, especially in the ancestral environment. For instance: https://s-media-cache-ak0.pinimg.com/736x/24/5b/59/245b59444cdcff14d139df7c37b7e271.jpg https://s-media-cache-ak0.pinimg.com/736x/24/5b/59/245b59444...
- im2w1l 11y ago
  I like this one http://0.media.collegehumor.cvcdn.com/70/36/483778211d5177e9b7b74dbc8bbc8290-amazing-floating-paper-cube-illusion.gif http://0.media.collegehumor.cvcdn.com/70/36/483778211d5177e9... It seems impossible a few seconds until the trick is revealed.
  - dr_zoidberg 11y ago
    I was expecting that (or the T-rex variation of) when he mentioned optical illusions. The image he posted is more related to perspective than how the visual cortex works -- that is, it can even be done inside a 3d game/engine, with clever texturing.
zardeh 11y ago
This would be a textbook example of overfitting, yes?
- IshKebab 11y ago
  Yeah I suppose you could say that our "training set" doesn't include optical illusions. Though I wouldn't really call it overfitting because our brains haven't learnt specific training inputs, which is what happens with overfitting.

nullc 11y ago

Search for explaining and harnessing adversarial examples for some contemplation on the overly confident results.

jkldotio 11y ago

I find it less interesting in terms of applications. I am sure finding the causes of these illusions will help in understanding aspects of how the system, human or machine, subject to the illusion operates; however, so many of them depend on either a static image or a fixed perspective that it just doesn't seem to be all that big of an issue to me. The visual illusions humans are subject to are frequently defeated by some simple motion to reposition the viewer and there seem to be few illusions that can sustain being in motion themselves and still fool people. From that it doesn't seem all that revolutionary that a fixed position view of a three dimensional reality, with no fourth dimension of time, is not omniscient. It only has one bias sample or view of the scene, so why would we expect it to be omniscient? The paper "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"[0] is interesting in its explorations of the issue but I still don't get the hype around "fooling" DNNs. Even if someone gets an actual video scene, a timeseries of frame after frame, that still fools some kind of DNN (perhaps a LSTM => softmax classification), it's still not all that interesting as that occasionally seems to happen to humans too. [0]http://arxiv.org/abs/1412.1897 http://arxiv.org/abs/1412.1897 (web, but the pdf linked from there is ~9.5MB)

amelius 11y ago

I'd say that the problem is not well-defined. You are asking to create a 3d reconstruction from 2*2d image data, so no wonder that some information has to be made up.