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Fix typo #28

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Fix typo #28

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2 changes: 1 addition & 1 deletion _posts/2014-07-03-feature-learning-escapades.markdown
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Expand Up @@ -54,7 +54,7 @@ This was also around the time when the Kinect came out, so I thought I'd give 3D

1. There is no obvious/clean way to plug a neural network into 3D data.
2. Reasoning about the difference between occluded / empty space is a huge pain.
3. It is very hard to collect data at scale. Neural nets love data and here I was playing around with datasets on order of 100 scenes, with no ideas about how this could be possibly scale.
3. It is very hard to collect data at scale. Neural nets love data and here I was playing around with datasets on order of 100 scenes, with no ideas about how this could be possibly scaled.
4. I was working with fully static 3D environments. No movement, no people, no fun.

I ended up doing a bit of Unsupervised Object Discovery in my 3D meshes and publishing it at a robotics conference, where it was most relevant (<a href="http://cs.stanford.edu/people/karpathy/discovery/">Object Discovery in 3D scenes via Shape Analysis</a>). I was happy that I found a very simple, efficient and surprisingly effective way of computing objectness over 3D meshes, but it wasn't what I set out to do. I followed up on the project a bit while working with Sebastian Thrun for my last rotation, but I remained unsatisfied and unfulfilled. There was no brain stuff, no huge datasets to learn from, and even if it all worked, it would work on static, boring scenes.
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