Permutohedral Lattice CNNs
2015
Conference Paper
ei
ps
This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.
Author(s): | Martin Kiefel and Varun Jampani and Peter V. Gehler |
Book Title: | ICLR Workshop Track |
Year: | 2015 |
Month: | May |
Day: | 7-9 |
Department(s): | Empirical Inference, Perceiving Systems |
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
Event Name: | ICLR 2015 |
Event Place: | San Diago |
State: | Published |
URL: | http://arxiv.org/abs/1412.6618 |
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BibTex @inproceedings{kiefel_iclr_2015, title = {Permutohedral Lattice CNNs}, author = {Kiefel, Martin and Jampani, Varun and Gehler, Peter V.}, booktitle = {ICLR Workshop Track}, month = may, year = {2015}, doi = {}, url = {http://arxiv.org/abs/1412.6618}, month_numeric = {5} } |