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

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

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

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

Conditional Random Fields as Recurrent Neural Networks https://arxiv.org/abs/1409.5403

I hope more fruit comes out of the fusion deep learning and graphical models.

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.

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).

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.

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.

"20 years back, everyone wanted CD's for music, today it is flash drives, the upcoming thing is streaming music from the internet. Technologies come and go. What I can say is that the Fourier transform will still be around 200 years from now as a valuable way of understanding the world."

I am of course in no real position to assert that this is a good idea (as I am still in graduate school), but this is the reason why I gravitate towards math and physics classes. Conditional on the assumption that we have a meaningful civilization decades from now (and none of the dangers outlined for instance in the excellent book "Here Be Dragons: Science, Technology and the Future of Humanity" by Olle Haggstrom) cause its utter collapse, I think it is a safe bet to assume "fundamental" topics like Maxwell's equations, Fourier transforms, etc will still be around, alive, and fruitfully studied and applied across a variety of domains. Part of the reason is simply historical: the Fourier transform has had over 200 years to prove its worth time and again across a variety of disciplines.

Maybe deep learning is here to stay, maybe it isn't. I don't know, and I am not willing or interested in making bets either way. I am willing to bet on a time proven framework of understanding, such as the examples above.

ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter them against the wall, and hope for the best.

The best, are things like "Tell what type of bird it is"... But in the end, we have no clue HOW it got those results. Just that the Mystery Magic Function + data = results.

I can certainly see a middle path of being able to train a learning function, and then interrogating said function for the underlying things that make it true. Once we understand the primitives, then we can make ideal functions that do X, and do it well. Because right now, learning functions do it rather well but with exceptional overhead. And they're hard to tune without recrunching the whole dataset.

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.

So it's actually worse than web development.

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.

Actually I'd argue that now deep learning requires the least investment of time and effort compared to potential payoff. Hell if you spend just a couple of weekends grinding through tensorflow tutorials you'll be able to impress the crap out of a huge number of people, many of them with far more money than sense.

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.

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.