What is the cost/benefit in this? Is it appropriate to divert resources to older techniques because we haven't yet figured how to do it in the new way? Yes we can score a few points but that is an advancement only to an academic setting.
We used to need PhDs to do simple Computer Vision applications and object recognition in a meaningful matter was a far away dream. Now a child can make an application on its Raspberry Pi, because the new way of doing things is generalizable. You don't need to spend huge amounts of time redoing things just to get to the basics and reach the state of the art as it was 20 years ago.
Should we reintroduce the old ways for the quick wins, or should we divert our research resources to try to solve unsolved problems? The GPU will get cheaper the cloud GPUs will be cheaper.
So this is the state of things today. If somebody wants to advertise his paper's submission to a conference good for him but it should not be presented as an important advancement that should be the new way of doing things. Because it isn't.
When we decide that its feasible to send robots to the planets or even build robots on them, building chessboards to do rectification of the cameras should not be one of their tasks.
Disclaimer: I had horse in this race too, I was on the losing side of the deep learning argument, I was wrong, I got over it.
> What is the cost/benefit in this? Is it appropriate to divert resources to older techniques because we haven't yet figured how to do it in the new way?
I too would like to know why cars have wheels when helicopters have shown that rotors work well. We should have abandoned wheels ages ago since they are clearly old and with that inferior.
> Now a child can make an application on its Raspberry Pi, because the new way of doing things is generalizable.
And that app will have issues deciding if it sees a couch or a leopard. Or do you expect a child to correctly train its neural net?
> The GPU will get cheaper the cloud GPUs will be cheaper.
Why again do we have multiple algorithms for sort when a few nested for loops would do? Maybe smart algorithms scale better than hardware ever could.
> building chessboards to do rectification of the cameras should not be one of their tasks.
And yet we have mars probes with a build in color table. Why do you hate geometry?
>I too would like to know why cars have wheels when helicopters have shown that rotors work well. We should have abandoned wheels ages ago since they are clearly old and with that inferior.
I said cost/benefit and your example is about an expensive way to do what a car can do. You get some points for speed but lose big points on affordable transportation. The world has settled on the car.
> And that app will have issues deciding if it sees a couch or a leopard. Or do you expect a child to correctly train its neural net?
It will train a neural net sooner than it will learn 3d computer vision.
> And yet we have mars probes with a build in color table. Why do you hate geometry?
I said that I was a skeptic against deep learning an in favor of the old ways, so why do you claim that I hate it? Its inadequacy regarding the current and possibly future state of computer vision technology is not an expression of my feelings. Its a mere observation.
> I said cost/benefit and your example is about an expensive way to do what a car can do.
Sometimes neural net running on a sever rack full of GPUs is also overkill, including the cost/benefit. You get bonus points for buzzwords thought.
> It will train a neural net sooner than it will learn 3d computer vision.
And you would trust it to run as required? I admire your courage. Unless the person selecting the training data knew what they were doing I wouldn't. I certainly wouldn't trust a child to get it right without being trained itself.
> I said that I was a skeptic against deep learning an in favor of the old ways, so why do you claim that I hate it?
Your mention against "building chessboards" for basic calibration as if that was in any way hard or even necessary. Calibration of sensors on mars is already a solved problem. I don't understand why you would think otherwise.
> I said cost/benefit and your example is about an expensive way to do what a car can do. You get some points for speed but lose big points on affordable transportation. The world has settled on the car.
No, the world uses the one that makes sense for the particular circumstance.
We used to need PhDs to do simple Computer Vision applications and object recognition in a meaningful matter was a far away dream. Now a child can make an application on its Raspberry Pi, because the new way of doing things is generalizable. You don't need to spend huge amounts of time redoing things just to get to the basics and reach the state of the art as it was 20 years ago.
Should we reintroduce the old ways for the quick wins, or should we divert our research resources to try to solve unsolved problems? The GPU will get cheaper the cloud GPUs will be cheaper.
So this is the state of things today. If somebody wants to advertise his paper's submission to a conference good for him but it should not be presented as an important advancement that should be the new way of doing things. Because it isn't.
When we decide that its feasible to send robots to the planets or even build robots on them, building chessboards to do rectification of the cameras should not be one of their tasks.
Disclaimer: I had horse in this race too, I was on the losing side of the deep learning argument, I was wrong, I got over it.