44,838 research outputs found
Improved Densification of One Permutation Hashing
The existing work on densification of one permutation hashing reduces the
query processing cost of the -parameterized Locality Sensitive Hashing
(LSH) algorithm with minwise hashing, from to merely ,
where is the number of nonzeros of the data vector, is the number of
hashes in each hash table, and is the number of hash tables. While that is
a substantial improvement, our analysis reveals that the existing densification
scheme is sub-optimal. In particular, there is no enough randomness in that
procedure, which affects its accuracy on very sparse datasets.
In this paper, we provide a new densification procedure which is provably
better than the existing scheme. This improvement is more significant for very
sparse datasets which are common over the web. The improved technique has the
same cost of for query processing, thereby making it strictly
preferable over the existing procedure. Experimental evaluations on public
datasets, in the task of hashing based near neighbor search, support our
theoretical findings
DeepPermNet: Visual Permutation Learning
We present a principled approach to uncover the structure of visual data by
solving a novel deep learning task coined visual permutation learning. The goal
of this task is to find the permutation that recovers the structure of data
from shuffled versions of it. In the case of natural images, this task boils
down to recovering the original image from patches shuffled by an unknown
permutation matrix. Unfortunately, permutation matrices are discrete, thereby
posing difficulties for gradient-based methods. To this end, we resort to a
continuous approximation of these matrices using doubly-stochastic matrices
which we generate from standard CNN predictions using Sinkhorn iterations.
Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet,
an end-to-end CNN model for this task. The utility of DeepPermNet is
demonstrated on two challenging computer vision problems, namely, (i) relative
attributes learning and (ii) self-supervised representation learning. Our
results show state-of-the-art performance on the Public Figures and OSR
benchmarks for (i) and on the classification and segmentation tasks on the
PASCAL VOC dataset for (ii).Comment: Accepted in IEEE International Conference on Computer Vision and
Pattern Recognition CVPR 201
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