7 ms·

Actually, what's significant about this work is that labeled training data was not required: "Contrary to what appears to be a widely-held intuition, our exper

Actually, what's significant about this work is that labeled training data was not required: "Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not."

Smerity 14y ago
I replied by adding to my comment above as it wouldn't allow me to reply earlier. Reference that. tldr; High-level features generated by this unsupervised network are so high-level that one of them aligns with "has a face in the image", another to "has cat in image" (see the section "Cat and human body detectors") and so on. Note however that they select the "best neuron" for face classification -- the only way they can do that is via using labelled data and testing all the neurons (where each neuron's activation is a feature). Thus, these features cannot be used without labelled training.
- reitzensteinm 14y ago
  But the difference is that you can show it 1 billion unclassified images, then show it 1000 images you know to be faces, analyzing how its neurons respond to the known inputs to use it to classify the rest of the images. Strictly speaking, you do need to have some labeled data at the end in order to determine how the neural net views faces, but I think that obscures what's notable about this system. The amount of human participation involved in training is potentially six or more orders of magnitude less. That's a breakthrough, and a change in kind, not just degree.
  - Smerity 14y ago
    In a more general response: I don't think what I stated obscures what's notable about the system, I feel I stated exactly what was notable and specifically avoided overstating it. Overhyping when it comes to machine learning and AI seems to be the norm and has already hurt AI/ML severely in the past[1]. More specifically: I didn't disagree with anything you've stated, simply pointed out that labeled training data is necessary in response to the statement that it wasn't.The high-level feature extraction the paper discusses is unsupervised but the classifiers it produces are semi-supervised. It's an important distinction. [1]: http://en.wikipedia.org/wiki/AI_winter http://en.wikipedia.org/wiki/AI_winter
    - sellsword 14y ago
      Having a bachelor's emphasis in AI, I think you described it perfectly. I was wondering too from their abstract how they were recognizing "faces" entirely without labels, this makes it clearer. As you said, unsupervised they can find extremely high-level categories. That is pretty impressive.
  - dmoney 14y ago
    How does this work? I thought neural nets only learned when they got some kind of feedback that let them know whether what their classification was right (back propagation).
    - Smerity 14y ago
      The neural network in this paper, an autoencoder, doesn't require labelled data. Autoencoders take high dimensional input, map it to a lower dimensional space and then try to recreate the original high dimensional input as closely as possible. The idea is to learn a compressed representation for the data and hope that this compressed representation works as a high level featureset. As the model is just trying to represent the original input, no labelled data is required for the initial part. Labelled data is later introduced when the high level features are used for classification. What's most interesting about this paper is that one of the features learned by the model maps quite well to "image contains a face" without any prompting by the researchers. For more details, check out http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf
JohnHaugeland 14y ago
Unsupervised training is not particularly significant, and was the original form of neural network. "our experimental results reveal" No, they don't. We had one of these in the 1980s.
- saalweachter 14y ago
  Did we? A lot of early works in AI were ... /overstated/.[1] While a lot of concepts were created way back when, a lot of results weren't really generated. It's extremely valuable for someone to actually go and do a thing, now that we can, even if someone had the idea for the thing eons ago. [1] http://dl.acm.org/citation.cfm?id=1045340 http://dl.acm.org/citation.cfm?id=1045340
  - JohnHaugeland 14y ago
    The early works in AI with regards to unsupervised learning were in the 1940s and 1950s. Claude Shannon had demonstrated a chess learning system which taught itself by playing him to defeat him in under two weeks as early as 1949. No, they weren't overstated. They were hyped by a clueless press. There's a pretty critical difference. It's a bit like how the early web pioneers didn't say that the web was going to revolutionize the delivery of dog food; it was a journalist who said that. "It's extremely valuable for someone to actually go and do a thing, now that we can" Self organized unsupervised learning was in use for optical classification of potatoes in the feeding of Frito Lay automated processing plants in the late 1970s. Please distinguish that you haven't actually looked for earlier examples from that you imagine none exist. Thanks.
    - redmoskito 14y ago
      I find both of your comments extremely condescending, both toward saalweachter and the authors of this article. 1. The fact that Claude Shannon succeeded in training a chess system has virtually no impact on sallweachter's claim that many AI results were overstated. 2. Certainly the press overstated them, which supports saalweachter's premise rather than weakening it. Even if the _implied_ claim was that _researchers_ overstated results, your argument does nothing to weaken this claim. 3. Frito Lay solved a problem several orders of magnitude easier that of face recognition in natural images, which is still very much an open problem in computer vision. 4. Similar to 1., the Frito Lay example contributes nothing to your goal of weakening saalweachter's claim that this is valuable research--a claim which is exceedingly innocuous. I understand that you've probably got a bone to pick against the many AI naysayers and saalweachter's comments conjured a few common misrepresentations (i.e. (a) that the "AI revolution" burnt-out because it's researchers were somehow naive and (b) that neural networks are something new invented by computer vision researchers). You'd be justified in arguing against these claims, and I'm sure your father (respected AI researcher of the same name) would make them too, if saalweachter had tried to make them (which he didn't). But even if you were justified in making the argument, I would expect a less condescending one that made better use of evidence than the argument you've made here.

Smerity 14y ago

I replied by adding to my comment above as it wouldn't allow me to reply earlier. Reference that. tldr; High-level features generated by this unsupervised network are so high-level that one of them aligns with "has a face in the image", another to "has cat in image" (see the section "Cat and human body detectors") and so on. Note however that they select the "best neuron" for face classification -- the only way they can do that is via using labelled data and testing all the neurons (where each neuron's activation is a feature). Thus, these features cannot be used without labelled training.

reitzensteinm 14y ago
But the difference is that you can show it 1 billion unclassified images, then show it 1000 images you know to be faces, analyzing how its neurons respond to the known inputs to use it to classify the rest of the images. Strictly speaking, you do need to have some labeled data at the end in order to determine how the neural net views faces, but I think that obscures what's notable about this system. The amount of human participation involved in training is potentially six or more orders of magnitude less. That's a breakthrough, and a change in kind, not just degree.
- Smerity 14y ago
  In a more general response: I don't think what I stated obscures what's notable about the system, I feel I stated exactly what was notable and specifically avoided overstating it. Overhyping when it comes to machine learning and AI seems to be the norm and has already hurt AI/ML severely in the past[1]. More specifically: I didn't disagree with anything you've stated, simply pointed out that labeled training data is necessary in response to the statement that it wasn't.The high-level feature extraction the paper discusses is unsupervised but the classifiers it produces are semi-supervised. It's an important distinction. [1]: http://en.wikipedia.org/wiki/AI_winter http://en.wikipedia.org/wiki/AI_winter
  - sellsword 14y ago
    Having a bachelor's emphasis in AI, I think you described it perfectly. I was wondering too from their abstract how they were recognizing "faces" entirely without labels, this makes it clearer. As you said, unsupervised they can find extremely high-level categories. That is pretty impressive.
- dmoney 14y ago
  How does this work? I thought neural nets only learned when they got some kind of feedback that let them know whether what their classification was right (back propagation).
  - Smerity 14y ago
    The neural network in this paper, an autoencoder, doesn't require labelled data. Autoencoders take high dimensional input, map it to a lower dimensional space and then try to recreate the original high dimensional input as closely as possible. The idea is to learn a compressed representation for the data and hope that this compressed representation works as a high level featureset. As the model is just trying to represent the original input, no labelled data is required for the initial part. Labelled data is later introduced when the high level features are used for classification. What's most interesting about this paper is that one of the features learned by the model maps quite well to "image contains a face" without any prompting by the researchers. For more details, check out http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf

JohnHaugeland 14y ago

Unsupervised training is not particularly significant, and was the original form of neural network. "our experimental results reveal" No, they don't. We had one of these in the 1980s.

saalweachter 14y ago
Did we? A lot of early works in AI were ... /overstated/.[1] While a lot of concepts were created way back when, a lot of results weren't really generated. It's extremely valuable for someone to actually go and do a thing, now that we can, even if someone had the idea for the thing eons ago. [1] http://dl.acm.org/citation.cfm?id=1045340 http://dl.acm.org/citation.cfm?id=1045340
- JohnHaugeland 14y ago
  The early works in AI with regards to unsupervised learning were in the 1940s and 1950s. Claude Shannon had demonstrated a chess learning system which taught itself by playing him to defeat him in under two weeks as early as 1949. No, they weren't overstated. They were hyped by a clueless press. There's a pretty critical difference. It's a bit like how the early web pioneers didn't say that the web was going to revolutionize the delivery of dog food; it was a journalist who said that. "It's extremely valuable for someone to actually go and do a thing, now that we can" Self organized unsupervised learning was in use for optical classification of potatoes in the feeding of Frito Lay automated processing plants in the late 1970s. Please distinguish that you haven't actually looked for earlier examples from that you imagine none exist. Thanks.
  - redmoskito 14y ago
    I find both of your comments extremely condescending, both toward saalweachter and the authors of this article. 1. The fact that Claude Shannon succeeded in training a chess system has virtually no impact on sallweachter's claim that many AI results were overstated. 2. Certainly the press overstated them, which supports saalweachter's premise rather than weakening it. Even if the _implied_ claim was that _researchers_ overstated results, your argument does nothing to weaken this claim. 3. Frito Lay solved a problem several orders of magnitude easier that of face recognition in natural images, which is still very much an open problem in computer vision. 4. Similar to 1., the Frito Lay example contributes nothing to your goal of weakening saalweachter's claim that this is valuable research--a claim which is exceedingly innocuous. I understand that you've probably got a bone to pick against the many AI naysayers and saalweachter's comments conjured a few common misrepresentations (i.e. (a) that the "AI revolution" burnt-out because it's researchers were somehow naive and (b) that neural networks are something new invented by computer vision researchers). You'd be justified in arguing against these claims, and I'm sure your father (respected AI researcher of the same name) would make them too, if saalweachter had tried to make them (which he didn't). But even if you were justified in making the argument, I would expect a less condescending one that made better use of evidence than the argument you've made here.