3 ms·

I'm curious what you mean by this, could you elaborate

sjtgraham 13y ago
Unsupervised learning using deep neural networks, e.g. Google made a face detector using only unlabelled images that trained itself[1]. Seems like a similar approach could be applied to image segmentation, i.e. this problem. [1] http://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised_icml2012.pdf http://static.googleusercontent.com/media/research.google.co...
- deutronium 13y ago
  Hmm, I think the difficulty would then be how you train the neural network, I'm not sure that'd be an easy task. I think you'd have to apply a fair amount of pre-processing anyway before you passed it over to the ANN.
  - sjtgraham 13y ago
    I think the difficulty is in the computing power required to train the net, but there has been some progress in that area lately http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html http://techblog.netflix.com/2014/02/distributed-neural-netwo...
    - vonnik 13y ago
      You're both right. There are challenges in training a deep-learning neural network, and that training requires a lot of processing power. We open sourced a pretty cool standalone machine in Java that addresses those issues about a week ago. Looking for feedback... http://deeplearning4j.org http://deeplearning4j.org
      - agibsonccc 13y ago
        DeepLearning4j author here. I'd just like to add that despite training neural networks being hard, they are great for understanding data if trained right. There are a lot of innovations in image processing wrt neural nets specifically. The right neural network can learn everything from scene detection to simple object recognition. I would highly reccommend taking a look at the neural nets course on coursera to understand some of the use cases.
- dclowd9901 13y ago
  You could probably do some neat things with Google glass. Feed the computer the images the human pays attention to all day, and let the computer extrapolate objects by how a persons' vision tracks them.
  - SimHacker 13y ago
    If by "all day" you mean "a couple of hours" that it takes to run down the batteries by capturing and processing live video.
boomzilla 13y ago
It's a new game in town: whenever someone says "big data", you reply "deep learning", someone says "mobile", you say "real time", someone says "sharing", you say "social", etc ...
- SimHacker 13y ago
  Hello. Greetings. Brrrrrrring!!! http://www.youtube.com/watch?v=KTc3PsW5ghQ http://www.youtube.com/watch?v=KTc3PsW5ghQ
- deleted 13y ago
  [deleted]

Unsupervised learning using deep neural networks, e.g. Google made a face detector using only unlabelled images that trained itself[1]. Seems like a similar approach could be applied to image segmentation, i.e. this problem. [1] http://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised_icml2012.pdf http://static.googleusercontent.com/media/research.google.co...

deutronium 13y ago
Hmm, I think the difficulty would then be how you train the neural network, I'm not sure that'd be an easy task. I think you'd have to apply a fair amount of pre-processing anyway before you passed it over to the ANN.
- sjtgraham 13y ago
  I think the difficulty is in the computing power required to train the net, but there has been some progress in that area lately http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html http://techblog.netflix.com/2014/02/distributed-neural-netwo...
  - vonnik 13y ago
    You're both right. There are challenges in training a deep-learning neural network, and that training requires a lot of processing power. We open sourced a pretty cool standalone machine in Java that addresses those issues about a week ago. Looking for feedback... http://deeplearning4j.org http://deeplearning4j.org
    - agibsonccc 13y ago
      DeepLearning4j author here. I'd just like to add that despite training neural networks being hard, they are great for understanding data if trained right. There are a lot of innovations in image processing wrt neural nets specifically. The right neural network can learn everything from scene detection to simple object recognition. I would highly reccommend taking a look at the neural nets course on coursera to understand some of the use cases.
dclowd9901 13y ago
You could probably do some neat things with Google glass. Feed the computer the images the human pays attention to all day, and let the computer extrapolate objects by how a persons' vision tracks them.
- SimHacker 13y ago
  If by "all day" you mean "a couple of hours" that it takes to run down the batteries by capturing and processing live video.

boomzilla 13y ago

It's a new game in town: whenever someone says "big data", you reply "deep learning", someone says "mobile", you say "real time", someone says "sharing", you say "social", etc ...

SimHacker 13y ago
Hello. Greetings. Brrrrrrring!!! http://www.youtube.com/watch?v=KTc3PsW5ghQ http://www.youtube.com/watch?v=KTc3PsW5ghQ
deleted 13y ago
[deleted]