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I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-le

by andreyk 10y ago

I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-learning-is-overrated sort of way. Whereas it seems to me he is merely saying Deep Learning should be informed by Classic CV. "I think we’re running out of low-hanging fruit, or problems we can solve with a simple high-level deep learning API. Specifically, I think many of the next advances in computer vision with deep learning will come from insights to geometry." And it's true. A lot of the low-hanging fruit have been gotten, and stuff like SLAM is not about to be done wholly by deep learning (probably). There are a lot of problems that require more insight and analysis than 'throw a deep network at a large dataset'. As he concludes "learning complicated representations with deep learning is easier and more effective if the architecture can be structured to leverage the geometric properties of the problem." And thinking about what the architecture should is basically the hard bit in deep learning.

amelius 10y ago
The sad parts, for me are: 1. Thinking about a deep learning architecture is really a different kind of research. It requires much guesswork, a lot of trial and error, and lots of waiting for training cycles to complete. For me this type of work is much less interesting than engineering a solution, even if the results are better. 2. Deep learning is quickly becoming a commodity. One can download libraries to do almost anything with neural networks. I fear that deep learning will become the new "web development" of the 90s. Everybody can do it (this is not really true just yet, but I suspect we are rapidly reaching an asymptote where it is true).
- TheSpiceIsLife 10y ago
  What is sad about 2? Other than reminiscing about the halcyon days of the web (which is a forever moving target of 'how it was when I grew up'), it's the present day internet, technically speaking, a marvel. What's exciting about deep learning becoming a commodity isn't seeing what everybody can do with it, but what the will be built standing on it's shoulders.
  - TeMPOraL 10y ago
    > the present day internet, technically speaking, a marvel The infrastructural parts are marvelous indeed; as they have been for decades. If you mean the web, it's a marvel... that it works at all. It's a "marvelous" pile of bad engineering bolted on as fast as humanly possible. I guess GP's worry is that if a field gets dominated by people with no respect for the quality of their work, the whole field will be built out of bad tools - which is in long-term harmful for everyone, specialists and laymen alike.
- dagw 10y ago
  2) would be awesome. If for example our hydrologists could easily take their extensive domain knowledge and experiment with layer of deep learning on top of that without having to go though the "high priests" that can only be a good thing in my mind. I might be very much an oddity among programmers, but I genuinely believe that the more 'programming' that can be done by non-programmers, who actually understand the domain they are trying to model, the better. If nothing else it would free up more time for programmers to work on actual hard problems where they have more to contribute.
  - amelius 10y ago
    My main concern is whether it deserves my time. Investing in deep learning now costs considerable time and effort, whereas in the near future when deep learning is a commodity, that investment gives no real advantage.
    - dagw 10y ago
      Once it becomes a commodity there will be a bigger market for your services and a greater appreciation for the value you add (assuming you're actually good enough to add value). There was a time when knowing how HTML tables worked was enough to get you a job, and knowing how to post a form to a cgi perl script made you an expert. It's getting easier and easier for more and more people to do more and more complicated things on the web every day, and still (and probably because of this) people whom are really good at it are commanding a higher and higher salary.
      - amelius 10y ago
        But my point is that we're reaching an asymptote, where knowing much more about deep learning gives only a marginal advantage. So it's actually worse than web development.
        dagw 10y ago
        knowing much more about deep learning gives only a marginal advantage. Sure, but that's mostly because deep learning is so new and 'magical' that most people outside the field can't spot the difference between someone having PhD in the subject and someone having worked though a couple of Tensorflow tutorials. As the field matures and its practical applications to various fields and industries becomes clearer, so will the value of knowing more about deep learning than your peers.
BooglyWoo 10y ago
"Classical" CV and deep-learning CV needn't be opposing one another. There are several cases in which the classical approach is emulated by deep networks - implementing the same carefully thought-out pipelines but in a way that leverages representations learned from huge datasets (which are undeniably very powerful). Some examples are: * Bags of convolutional features for scalable instance search https://arxiv.org/pdf/1604.04653.pdf https://arxiv.org/pdf/1604.04653.pdf This paper treats each 'pixel' of a CNN activation tensor as a local descriptor, clusters them, and describes an image as a bag-of-visual-words histogram. * Learned Invariant Feature Transform https://arxiv.org/abs/1603.09114v2 https://arxiv.org/abs/1603.09114v2 This paper very explicitly emulates the entire SIFT pipeline for computing correspondences across pairs of images * Inverse compositional spatial transformer networks https://arxiv.org/abs/1612.03897v1 https://arxiv.org/abs/1612.03897v1 This paper emulates Lucas-Kanade approach to computing the transform between 2 images with differentiable (trainable) components. Also, don't forget that deformable part models are convolutional networks! https://arxiv.org/abs/1409.5403 https://arxiv.org/abs/1409.5403
- 3390778 10y ago
  Thank you for these great links. I'll add another interesting paper that carries on this emulation program: Conditional Random Fields as Recurrent Neural Networks https://arxiv.org/abs/1409.5403 https://arxiv.org/abs/1409.5403 I hope more fruit comes out of the fusion deep learning and graphical models.
jph00 10y ago
> I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-learning-is-overrated sort of way. Whereas it seems to me he is merely saying Deep Learning should be informed by Classic CV. Thank you for saying this! You were indeed prescient - nearly all the comments so far at total non-sequiturs based on what the article actually shows. Folks, please take a look at the actual GC-Net architecture, which is very nicely shown in the last figure in the post. As you'll see, it's still an end-to-end learnt deep learning model. The extremely neat trick is to come up with a way to represent the cost volume piece (that connects the 2d and 3d parts of the CNN) as a differentiable function, which allows it to be part of an end-to-end learnt neural network. This approach, of finding differentiable forms (often approximations) of domain-specific loss functions, transformations, etc so they can be inserted into neural nets, is a very powerful and increasingly widely used technique. It's not about replacing domain knowledge with deep learning, and it's definitely not about replacing deep learning with domain knowledge - it's all about using both. So in this case, the research is showing how to use geometry plus deep learning, not instead of deep learning.
deleted 10y ago
[deleted]

by andreyk 10y ago

amelius 10y ago

The sad parts, for me are: 1. Thinking about a deep learning architecture is really a different kind of research. It requires much guesswork, a lot of trial and error, and lots of waiting for training cycles to complete. For me this type of work is much less interesting than engineering a solution, even if the results are better. 2. Deep learning is quickly becoming a commodity. One can download libraries to do almost anything with neural networks. I fear that deep learning will become the new "web development" of the 90s. Everybody can do it (this is not really true just yet, but I suspect we are rapidly reaching an asymptote where it is true).

TheSpiceIsLife 10y ago
What is sad about 2? Other than reminiscing about the halcyon days of the web (which is a forever moving target of 'how it was when I grew up'), it's the present day internet, technically speaking, a marvel. What's exciting about deep learning becoming a commodity isn't seeing what everybody can do with it, but what the will be built standing on it's shoulders.
- TeMPOraL 10y ago
  > the present day internet, technically speaking, a marvel The infrastructural parts are marvelous indeed; as they have been for decades. If you mean the web, it's a marvel... that it works at all. It's a "marvelous" pile of bad engineering bolted on as fast as humanly possible. I guess GP's worry is that if a field gets dominated by people with no respect for the quality of their work, the whole field will be built out of bad tools - which is in long-term harmful for everyone, specialists and laymen alike.
dagw 10y ago
2) would be awesome. If for example our hydrologists could easily take their extensive domain knowledge and experiment with layer of deep learning on top of that without having to go though the "high priests" that can only be a good thing in my mind. I might be very much an oddity among programmers, but I genuinely believe that the more 'programming' that can be done by non-programmers, who actually understand the domain they are trying to model, the better. If nothing else it would free up more time for programmers to work on actual hard problems where they have more to contribute.
- amelius 10y ago
  My main concern is whether it deserves my time. Investing in deep learning now costs considerable time and effort, whereas in the near future when deep learning is a commodity, that investment gives no real advantage.
  - dagw 10y ago
    Once it becomes a commodity there will be a bigger market for your services and a greater appreciation for the value you add (assuming you're actually good enough to add value). There was a time when knowing how HTML tables worked was enough to get you a job, and knowing how to post a form to a cgi perl script made you an expert. It's getting easier and easier for more and more people to do more and more complicated things on the web every day, and still (and probably because of this) people whom are really good at it are commanding a higher and higher salary.
    - amelius 10y ago
      But my point is that we're reaching an asymptote, where knowing much more about deep learning gives only a marginal advantage. So it's actually worse than web development.
      - dagw 10y ago
        knowing much more about deep learning gives only a marginal advantage. Sure, but that's mostly because deep learning is so new and 'magical' that most people outside the field can't spot the difference between someone having PhD in the subject and someone having worked though a couple of Tensorflow tutorials. As the field matures and its practical applications to various fields and industries becomes clearer, so will the value of knowing more about deep learning than your peers.

BooglyWoo 10y ago

"Classical" CV and deep-learning CV needn't be opposing one another. There are several cases in which the classical approach is emulated by deep networks - implementing the same carefully thought-out pipelines but in a way that leverages representations learned from huge datasets (which are undeniably very powerful). Some examples are: * Bags of convolutional features for scalable instance search https://arxiv.org/pdf/1604.04653.pdf https://arxiv.org/pdf/1604.04653.pdf This paper treats each 'pixel' of a CNN activation tensor as a local descriptor, clusters them, and describes an image as a bag-of-visual-words histogram. * Learned Invariant Feature Transform https://arxiv.org/abs/1603.09114v2 https://arxiv.org/abs/1603.09114v2 This paper very explicitly emulates the entire SIFT pipeline for computing correspondences across pairs of images * Inverse compositional spatial transformer networks https://arxiv.org/abs/1612.03897v1 https://arxiv.org/abs/1612.03897v1 This paper emulates Lucas-Kanade approach to computing the transform between 2 images with differentiable (trainable) components. Also, don't forget that deformable part models are convolutional networks! https://arxiv.org/abs/1409.5403 https://arxiv.org/abs/1409.5403

3390778 10y ago
Thank you for these great links. I'll add another interesting paper that carries on this emulation program: Conditional Random Fields as Recurrent Neural Networks https://arxiv.org/abs/1409.5403 https://arxiv.org/abs/1409.5403 I hope more fruit comes out of the fusion deep learning and graphical models.

jph00 10y ago

> I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-learning-is-overrated sort of way. Whereas it seems to me he is merely saying Deep Learning should be informed by Classic CV. Thank you for saying this! You were indeed prescient - nearly all the comments so far at total non-sequiturs based on what the article actually shows. Folks, please take a look at the actual GC-Net architecture, which is very nicely shown in the last figure in the post. As you'll see, it's still an end-to-end learnt deep learning model. The extremely neat trick is to come up with a way to represent the cost volume piece (that connects the 2d and 3d parts of the CNN) as a differentiable function, which allows it to be part of an end-to-end learnt neural network. This approach, of finding differentiable forms (often approximations) of domain-specific loss functions, transformations, etc so they can be inserted into neural nets, is a very powerful and increasingly widely used technique. It's not about replacing domain knowledge with deep learning, and it's definitely not about replacing deep learning with domain knowledge - it's all about using both. So in this case, the research is showing how to use geometry plus deep learning, not instead of deep learning.

deleted 10y ago

[deleted]