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If he ever tries to train deeper models in this manner and test them on real video frames from a car, he will be in for some unpleasant surprises. Learning to

by pakl 10y ago

If he ever tries to train deeper models in this manner and test them on real video frames from a car, he will be in for some unpleasant surprises. Learning to map discrete snapshots of objects to labels won't yield a system that can deal with e.g., the reality of lighting conditions that a car will experience.

waleedka 10y ago
Author here. This first part is simple by design. It's targeted to those getting started in the field.
- pakl 10y ago
  Oh-- my comment wasn't about the simplicity of the first part. (In fact, this is a great tutorial, thanks for posting.) My comment is about the approach of using supervised learning to map directly from images to category labels.
  - waleedka 10y ago
    I'm not sure I'm following. Are you saying that using supervised learning is the wrong approach here? What would you use instead?
    - pakl 10y ago
      Yes, that's right! The way you are using supervised learning here will force the neural networks to map from textures directly to human labels. A purely feedforward network, no matter how deep, can only rote memorize the effects of the world on the images (viewing angle, lighting, etc) and will not generalize. Another shortcoming of feedforward nets is they cannot change how they interpret local features based on integrated global aspects of a scene, like ambient lighting or backlighting. As a result the network will fail to classify on new real world images. If instead you use recurrence to learn features that take the dynamical and global effects into account, you'll have a better chance of success. One example of how we did his is here [1]. [1] http://blog.piekniewski.info/2016/11/04/predictive-vision-in-a-nutshell/ http://blog.piekniewski.info/2016/11/04/predictive-vision-in...
      - nomel 10y ago
        I know very little of machine learning, so... It seems that your system is supervised for the initial training. Once the system is somewhat trained, is it possible to let it free with unsupervised training, say if the confidence is in some higher range, between some frames? For example, say there was a period of frames with very high confidence, some slightly occlusion or shadow that lowered the confidence, and then another period of high confidence. With something like motion prediction, and some confidence in where the sign was, could you use that period of lower confidence to help train, maybe with some verification from a knows, complicated, supervised data set? tldr; Are there methods to allow these systems to keep learning once they're deployed?
      - nomel 10y ago
        I know very little of machine learning, so... It seems that your system is supervised for the initial training. Once the system is somewhat trained, is it possible to let it free with unsupervised training, say if the confidence is in some higher range, between some frames? For example, say there was a period of frames with very high confidence, some slightly occlusion or shadow that lowered the confidence, and then another period of high confidence. With something like motion prediction, and some confidence in where the sign was, could you use that period of lower confidence to help train, maybe with some verification from a knows, complicated, supervised data set? tldr; Are there methods to allow these systems to keep learning once they're deployed? edit: And this may interest you, the brain appears to predict motion: https://whitneylab.berkeley.edu/people/gerrit/MausNijhawan.PsychScience.2008.pdf https://whitneylab.berkeley.edu/people/gerrit/MausNijhawan.P...
        felippee 10y ago
        Its rather the opposite. It is unsupervised initially, just learns to predict its input. Note there is confusion: the unit itself is using supervised (by the future signal) but all in all nothing needs to be labeled cause the reality just unfolds. Then, once this is done, one can use the trained features (representations) to train supervised tasks, such as the street sign tracking. PS: yes, there is a strong literature suggesting that the brain is predicting a bunch of things. Check this long review paper http://www.fil.ion.ucl.ac.uk/~karl/Whatever%20next.pdf http://www.fil.ion.ucl.ac.uk/~karl/Whatever%20next.pdf for plenty ideas and details.

by pakl 10y ago

waleedka 10y ago

Author here. This first part is simple by design. It's targeted to those getting started in the field.

pakl 10y ago
Oh-- my comment wasn't about the simplicity of the first part. (In fact, this is a great tutorial, thanks for posting.) My comment is about the approach of using supervised learning to map directly from images to category labels.
- waleedka 10y ago
  I'm not sure I'm following. Are you saying that using supervised learning is the wrong approach here? What would you use instead?
  - pakl 10y ago
    Yes, that's right! The way you are using supervised learning here will force the neural networks to map from textures directly to human labels. A purely feedforward network, no matter how deep, can only rote memorize the effects of the world on the images (viewing angle, lighting, etc) and will not generalize. Another shortcoming of feedforward nets is they cannot change how they interpret local features based on integrated global aspects of a scene, like ambient lighting or backlighting. As a result the network will fail to classify on new real world images. If instead you use recurrence to learn features that take the dynamical and global effects into account, you'll have a better chance of success. One example of how we did his is here [1]. [1] http://blog.piekniewski.info/2016/11/04/predictive-vision-in-a-nutshell/ http://blog.piekniewski.info/2016/11/04/predictive-vision-in...
    - nomel 10y ago
      I know very little of machine learning, so... It seems that your system is supervised for the initial training. Once the system is somewhat trained, is it possible to let it free with unsupervised training, say if the confidence is in some higher range, between some frames? For example, say there was a period of frames with very high confidence, some slightly occlusion or shadow that lowered the confidence, and then another period of high confidence. With something like motion prediction, and some confidence in where the sign was, could you use that period of lower confidence to help train, maybe with some verification from a knows, complicated, supervised data set? tldr; Are there methods to allow these systems to keep learning once they're deployed?
    - nomel 10y ago
      I know very little of machine learning, so... It seems that your system is supervised for the initial training. Once the system is somewhat trained, is it possible to let it free with unsupervised training, say if the confidence is in some higher range, between some frames? For example, say there was a period of frames with very high confidence, some slightly occlusion or shadow that lowered the confidence, and then another period of high confidence. With something like motion prediction, and some confidence in where the sign was, could you use that period of lower confidence to help train, maybe with some verification from a knows, complicated, supervised data set? tldr; Are there methods to allow these systems to keep learning once they're deployed? edit: And this may interest you, the brain appears to predict motion: https://whitneylab.berkeley.edu/people/gerrit/MausNijhawan.PsychScience.2008.pdf https://whitneylab.berkeley.edu/people/gerrit/MausNijhawan.P...
      - felippee 10y ago
        Its rather the opposite. It is unsupervised initially, just learns to predict its input. Note there is confusion: the unit itself is using supervised (by the future signal) but all in all nothing needs to be labeled cause the reality just unfolds. Then, once this is done, one can use the trained features (representations) to train supervised tasks, such as the street sign tracking. PS: yes, there is a strong literature suggesting that the brain is predicting a bunch of things. Check this long review paper http://www.fil.ion.ucl.ac.uk/~karl/Whatever%20next.pdf http://www.fil.ion.ucl.ac.uk/~karl/Whatever%20next.pdf for plenty ideas and details.