5 ms·

Why can't you just train the network on artificially altered photos, with "elephants" randomly scattered around, until it is robust to them?

deleted 8y ago
[deleted]
throwaway2048 8y ago
Are you going to repeat this with every object that exists, or could exist?
- olodus 8y ago
  No, you see neural networks are specially afraid of elephants. Just like elephants are especially afraid of mice. And so the circle continues, all the way around to humans who are especially afraid of neural networks and what they will do to society.
  - weregiraffe 8y ago
    I am sorry to inform you that this post is not that funny.
- weregiraffe 8y ago
  What I'd do is train it on a large diverse set of foreign objects, then test it with a different diverse set of foreign objects, to see if it is possible to make it generally robust. Basically, the goal is that any feature which is not overlapping with the "elephant" should not be influenced by it.
  - YeGoblynQueenne 8y ago
    Well, CNNs are already trained on "a large diverse set" of objects, in many different configurations. By the way, something that is not described in the article- the referenced paper also describes experiments where objects where translated inside the source image itself - so not in another image. And the tested networks were still confused. So it's not "foreign" objects that are the problem. It's that neural nets generalise very poorly. Show them something they haven't seen before and their ability to recognise it falls off a cliff.
hnhg 8y ago
Because there's always another type of 'elephant' to be thrown in. The networks are just not inherently robust against unexpected features (I'll call them outliers for want of a better term), and this is what needs to be fixed, and not increasing the variety and frequency of outliers.
- wmeredith 8y ago
  Doesn’t increasing the frequency and variety of outliers just make them no longer outliers?
  - hnhg 8y ago
    Indeed.
- weregiraffe 8y ago
  >Because there's always another type of 'elephant' to be thrown in. Well, you could randomly generate and render 3D objects, and paste them into photos. This will give you a huge space of 'elephants'.
  - QML 8y ago
    That would not seem computationally efficient if for every class (i.e. object) you were to create a 3D modeling and paste it into a training set
  - YeGoblynQueenne 8y ago
    If what you propose was possible to do, if it was possible to overcome the problem described in the above article by using 3d models of real-world objects, then it would also be possible to train on those models in the first place, i.e. we wouldn't need to train machine vision algorithms like CNNs on large datasets of digital stills or photographs- we'd just generate millions of 3d models of objects and scenes of interest, already fully annotated for supervised learning, and train on those. But, as you can probably tell, this is not what happens and instead we have to make do with "real" images, painstakingly collected and annotated with the classes of objets within them "by hand". What you propose is, essentially, training on a simulation. That doesn't work very well because statistical machine learning algorithms generalise very poorly from training data to unseen data and when your training data is a simulation (such as a 3d model of an object) and the unseen data is the real world (such as a video still, or a digitial photograph of that object) the resulting model will be very bad at predicting the unseen data, so identifying objects in images of the real world. It might seem to you (and to me!) that modern 3d simulations (as in high-end CGI and video games) is a very faithful simulation of the real world, but that's primarily because we look at modern 3d through human eyes and perceive it with our (presumably) human brains. We don't know how that works exactly, so we can't reproduce the process in computers, yet, therefore machine vision systems essentially "see" and "perceive" something completely different than what we do. In the end, what looks like a very close approximation to you, is pretty much useless for them- or in any case, they are not able to learn to generalise from that to real objects.
sweezyjeezy 8y ago
This study shows something more fundamental about the way that neural networks process images, other than just that random elephants are a problem. Rather than break the image down into constituent parts and reason about them individually, they use global context to help them classify. This lets them outperform humans on some tasks. The problem is if you show it something completely different to anything it has ever seen before (like a T-rex with a cane and monocle riding a bear), it will not reliably output something sensible. To an extent it shows the gap between neural networks being able to classify things, and actually being able to understand what they are seeing, which will always limit their utility beyond classifying images that look like other images they've seen before.
- salty_biscuits 8y ago
  I just spent 15 minutes putting a picture of a bear with a top hat riding Abraham Lincoln with laser eyes into all the online demos I could find. They weren't unintelligible responses, just a bit crap. They said "weapon", "animal", "person", etc. I'm not sure how much global context they really use, in my experience it has been latching onto a weird bit of local texture. More work to do...
  - sweezyjeezy 8y ago
    Try this one : https://i.ytimg.com/vi/I11Vlcpz3jc/hqdefault.jpg https://i.ytimg.com/vi/I11Vlcpz3jc/hqdefault.jpg I tried it on a couple of online demos, neither spotted that there was a car in the picture.
    - unstuckdev 8y ago
      Is a car still a car if it's taken out of context? To us, yes. To an AI, it might not matter. A space collision avoidance AI that identifies a road vehicle as a road vehicle and expects it to behave as one normally behaves would create problems. A human can look at a 1936 Ford truck floating in space and know it's not going to make a sudden left turn. An AI working in space would still treat it as debris if a human told it "that's a truck, it's not going to do anything different unless acted upon."
TeMPOraL 8y ago
Because that's not the problem. The issue is that networks can't recognize their output doesn't make sense, reconfigure themselves and try again, and repeat that process until the output starts matching expectations (while simultaneously weakening those expectations the longer this continues). The way our vision does.

deleted 8y ago

[deleted]

throwaway2048 8y ago

Are you going to repeat this with every object that exists, or could exist?

olodus 8y ago
No, you see neural networks are specially afraid of elephants. Just like elephants are especially afraid of mice. And so the circle continues, all the way around to humans who are especially afraid of neural networks and what they will do to society.
- weregiraffe 8y ago
  I am sorry to inform you that this post is not that funny.
weregiraffe 8y ago
What I'd do is train it on a large diverse set of foreign objects, then test it with a different diverse set of foreign objects, to see if it is possible to make it generally robust. Basically, the goal is that any feature which is not overlapping with the "elephant" should not be influenced by it.
- YeGoblynQueenne 8y ago
  Well, CNNs are already trained on "a large diverse set" of objects, in many different configurations. By the way, something that is not described in the article- the referenced paper also describes experiments where objects where translated inside the source image itself - so not in another image. And the tested networks were still confused. So it's not "foreign" objects that are the problem. It's that neural nets generalise very poorly. Show them something they haven't seen before and their ability to recognise it falls off a cliff.

hnhg 8y ago

Because there's always another type of 'elephant' to be thrown in. The networks are just not inherently robust against unexpected features (I'll call them outliers for want of a better term), and this is what needs to be fixed, and not increasing the variety and frequency of outliers.

wmeredith 8y ago
Doesn’t increasing the frequency and variety of outliers just make them no longer outliers?
- hnhg 8y ago
  Indeed.
weregiraffe 8y ago
>Because there's always another type of 'elephant' to be thrown in. Well, you could randomly generate and render 3D objects, and paste them into photos. This will give you a huge space of 'elephants'.
- QML 8y ago
  That would not seem computationally efficient if for every class (i.e. object) you were to create a 3D modeling and paste it into a training set
- YeGoblynQueenne 8y ago
  If what you propose was possible to do, if it was possible to overcome the problem described in the above article by using 3d models of real-world objects, then it would also be possible to train on those models in the first place, i.e. we wouldn't need to train machine vision algorithms like CNNs on large datasets of digital stills or photographs- we'd just generate millions of 3d models of objects and scenes of interest, already fully annotated for supervised learning, and train on those. But, as you can probably tell, this is not what happens and instead we have to make do with "real" images, painstakingly collected and annotated with the classes of objets within them "by hand". What you propose is, essentially, training on a simulation. That doesn't work very well because statistical machine learning algorithms generalise very poorly from training data to unseen data and when your training data is a simulation (such as a 3d model of an object) and the unseen data is the real world (such as a video still, or a digitial photograph of that object) the resulting model will be very bad at predicting the unseen data, so identifying objects in images of the real world. It might seem to you (and to me!) that modern 3d simulations (as in high-end CGI and video games) is a very faithful simulation of the real world, but that's primarily because we look at modern 3d through human eyes and perceive it with our (presumably) human brains. We don't know how that works exactly, so we can't reproduce the process in computers, yet, therefore machine vision systems essentially "see" and "perceive" something completely different than what we do. In the end, what looks like a very close approximation to you, is pretty much useless for them- or in any case, they are not able to learn to generalise from that to real objects.

sweezyjeezy 8y ago

This study shows something more fundamental about the way that neural networks process images, other than just that random elephants are a problem. Rather than break the image down into constituent parts and reason about them individually, they use global context to help them classify. This lets them outperform humans on some tasks. The problem is if you show it something completely different to anything it has ever seen before (like a T-rex with a cane and monocle riding a bear), it will not reliably output something sensible. To an extent it shows the gap between neural networks being able to classify things, and actually being able to understand what they are seeing, which will always limit their utility beyond classifying images that look like other images they've seen before.

salty_biscuits 8y ago
I just spent 15 minutes putting a picture of a bear with a top hat riding Abraham Lincoln with laser eyes into all the online demos I could find. They weren't unintelligible responses, just a bit crap. They said "weapon", "animal", "person", etc. I'm not sure how much global context they really use, in my experience it has been latching onto a weird bit of local texture. More work to do...
- sweezyjeezy 8y ago
  Try this one : https://i.ytimg.com/vi/I11Vlcpz3jc/hqdefault.jpg https://i.ytimg.com/vi/I11Vlcpz3jc/hqdefault.jpg I tried it on a couple of online demos, neither spotted that there was a car in the picture.
  - unstuckdev 8y ago
    Is a car still a car if it's taken out of context? To us, yes. To an AI, it might not matter. A space collision avoidance AI that identifies a road vehicle as a road vehicle and expects it to behave as one normally behaves would create problems. A human can look at a 1936 Ford truck floating in space and know it's not going to make a sudden left turn. An AI working in space would still treat it as debris if a human told it "that's a truck, it's not going to do anything different unless acted upon."

TeMPOraL 8y ago

Because that's not the problem. The issue is that networks can't recognize their output doesn't make sense, reconfigure themselves and try again, and repeat that process until the output starts matching expectations (while simultaneously weakening those expectations the longer this continues). The way our vision does.