Archive for January, 2013

Face Recognition by Yima and Features of Andrew Ng’s recent work.


Was thinking in terms of Andrew Ng’s “Building High-level Features Using Large Scale Unsupervised Learning (NIPS 2012)  and the Yima’s Robust Face Recognition (code present my other blog)

What could be benefits of using the features coming from the Andrew Ng and explicitly modeling them using the Sparse dictionary learning. Definitely one cannot use the the Dictionary as done by the Yima, since that is not feasible for huge amount of data and people. So will the features coming from the Andrew Ng’s work provide the robustness when used for the dictionary learning and then the coding?

Or Group Sparse coding and Block Dictionary learning could be used to better model the network itself, thus reducing the complexity and time required to train the network?

Just a thought.

NIPS 2012: Multimodal Learning with Deep Boltzmann Machines


This is quite interesting paper from from the Ruslan (Toronto University) ( project page: http://www.cs.toronto.edu/~nitish/multimodal/,  video-lecture http://videolectures.net/nips2012_salakhutdinov_multimodal_learning/) [they used Gaussian RBM while making DBM]

Interesting interms of application and how the DBM is used.

multi modal DBM

In this way they can use it given one set of features to find others. I will recommend watching the video lecture.

Vision and Deep Learning in 2012


This entry is an effort to collect important Deep Learning Papers that were published in 2012 especially related to computer vision.

There is general resource http://deeplearning.net/ but not a good resource that collects the papers in Deep Learning w.r.t to Computer Vision problems.

General Resources 

Interesting Papers