Deva's Sexy Part Marginals
Today, Rob Fergus gave a talk on his most recent deep learning research. This research topic has been promulgated by hardcore machine learning titans such as Andrew Ng, Yann LeCun, and Geoff Hinton, so it will be exciting to see how Rob applies these ideas to object recognition. Unsupervised Feature Learning seems pretty exciting; unfortunately, I just cannot take results on Caltech101/Caltech256 very seriously :-(
Deep Learning Learning Architectures
Hi,
ReplyDeleteI was wondering what was the presentation of Rob Fergus that gave to you in unsupervised feature learning. Is it published work, or not just yet?
Stratis
Hi Stratis,
ReplyDeleteThe most relevant paper (about deconvolutional networks) is the following:
M. Zeiler, D. Krishnan, G. Taylor, R. Fergus. Deconvolutional Networks. CVPR 2010.
-Tomasz
What are the concerns about CalTech 101 and 256? 101 especially is a common benchmark in Machine Learning. What about Cifar 10?
ReplyDeleteCaltech 101/256 represent a certain special but limited view of the world. In particular, these datasets generally present a single visual concept per image (highly stylized image) and it is unclear whether progress on these datasets will enable machines to understand the visual world in a way which rivals human performance.
ReplyDeleteFurther reading on dataset bias:
A. Torralba, A.A. Efros, ``Unbiased Look at Dataset Bias'', Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, June 2011
Here is a followup from this post:
ReplyDeleteThe paper which Rob Fergus talked about at CMU is the following:
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
Matthew D. Zeiler, Graham W. Taylor, and Rob Fergus
International Conference on Computer Vision(November 6-13, 2011)
Matthew Zeiler is the first author and at the time of the presentation, the paper wasn't out so I had given a citation to an earlier paper.