The Neural Net Tank Urban Legend
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The Neural Net Tank Urban Legend
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Re: The Neural Net Tank Urban Legend
#2[deleted]
Re: The Neural Net Tank Urban Legend
#3The big takeway for me is that even if untrue, similar situations are true. Like this one from the article: "Gender-From-Iris or Gender-From-Mascara?" https://arxiv.org/pdf/1702.01304.pdf
Re: The Neural Net Tank Urban Legend
#4It's funny, I heard this legend a bunch, but stopped hearing it after the 2012 AlexNet paper.
Re: The Neural Net Tank Urban Legend
#5I get the author's feelings about why we shouldn't tell this story, but I still disagree. It's a pithy, funny example of GIGO in machine learning. People could read conclusions about the abilities of neural networks from the story, but they're wrong to do so -- it's a PEBKAC error, not a technology one. "Truthy" cautionary tales are a near-universal feature of human cultures -- why shouldn't machine learning have some?
Re: The Neural Net Tank Urban Legend
#6>I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning
I don't see how this story gives a "misleading" view of deep learning. From my (admittedly limited) experience with self-driving RC cars, this type of mistake is quite easy for a neural net to make while being quite difficult to detect. In our case, after utilizing a visual back-prop method, we realized our car was using the lights above to direct itself rather than the lanes on the road.
Now, you can refute this and say "well clearly your data wasn't extensive enough" or "your behavioral model is too simple for a complicated task like driving" however as these tools become easier to use, more and more organizations will put them into practice without as much care as the researchers behind most of the current production efforts.
Re: The Neural Net Tank Urban Legend
#7It's funny, I heard this legend a bunch, but stopped hearing it after the 2012 AlexNet paper.
For reference:
AlexNet[1] is the name of a convolutional neural network, originally written with CUDA to run with GPU support, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points ahead of the runner up. AlexNet was designed by the SuperVision group, consisting of Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever.
AlexNet Paper(PDF)[0]
[0]: http://vision.stanford.edu/teaching/cs231b_spring1415/slides... [1]: https://en.wikipedia.org/wiki/AlexNet
Re: The Neural Net Tank Urban Legend
#8>I suggest that dataset bias is real but exaggerated by the tank story, giving a misleading indication of risks from deep learning I don't see how this story gives a "misleading" view of deep learning. From my (admittedly limited) experience with self-driving RC cars, this type of mistake is quite easy for a neural net to make while being quite difficult to detect. In our case, after utilizing a visual back-prop meth…
I assume you're referring to some simple lane-keeping CNN where the CNN predicts steering angle from a video recording+human inputs: and yes, your dataset isn't extensive enough, and you'll never have enough data either, not due to some amusing bias in your CNN or taking shortcuts, but because it's a reinforcement learning problem and not a classification problem - your RC CNN could learn a better model of the road which doesn't involve lights at all and it won't make any real difference, it'll still be unable to correct for its errors or adapt to new situations and crash.
Re: The Neural Net Tank Urban Legend
#9> a common preprocessing step in computer vision (and NNs in general) is to whiten the image by standardizing or transforming pixels to a normal distribution; this would tend to wipe global brightness levels, promoting invariance to illumination
Is there anybody still doing this?
Re: The Neural Net Tank Urban Legend
#10> a common preprocessing step in computer vision (and NNs in general) is to whiten the image by standardizing or transforming pixels to a normal distribution; this would tend to wipe global brightness levels, promoting invariance to illumination Is there anybody still doing this?
Seems to still be pretty common: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C21&as_yl... Am I wrong?