3 research outputs found
Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations
It is a widely accepted fact that data representations intervene noticeably
in machine learning tools. The more they are well defined the better the
performance results are. Feature extraction-based methods such as autoencoders
are conceived for finding more accurate data representations from the original
ones. They efficiently perform on a specific task in terms of 1) high accuracy,
2) large short term memory and 3) low execution time. Echo State Network (ESN)
is a recent specific kind of Recurrent Neural Network which presents very rich
dynamics thanks to its reservoir-based hidden layer. It is widely used in
dealing with complex non-linear problems and it has outperformed classical
approaches in a number of tasks including regression, classification, etc. In
this paper, the noticeable dynamism and the large memory provided by ESN and
the strength of Autoencoders in feature extraction are gathered within an ESN
Recurrent Autoencoder (ESN-RAE). In order to bring up sturdier alternative to
conventional reservoir-based networks, not only single layer basic ESN is used
as an autoencoder, but also Multi-Layer ESN (ML-ESN-RAE). The new features,
once extracted from ESN's hidden layer, are applied to classification tasks.
The classification rates rise considerably compared to those obtained when
applying the original data features. An accuracy-based comparison is performed
between the proposed recurrent AEs and two variants of an ELM feed-forward AEs
(Basic and ML) in both of noise free and noisy environments. The empirical
study reveals the main contribution of recurrent connections in improving the
classification performance results.Comment: 13 pages, 9 figure
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
Analyzing the geometric and semantic properties of 3D point clouds through
the deep networks is still challenging due to the irregularity and sparsity of
samplings of their geometric structures. This paper presents a new method to
define and compute convolution directly on 3D point clouds by the proposed
annular convolution. This new convolution operator can better capture the local
neighborhood geometry of each point by specifying the (regular and dilated)
ring-shaped structures and directions in the computation. It can adapt to the
geometric variability and scalability at the signal processing level. We apply
it to the developed hierarchical neural networks for object classification,
part segmentation, and semantic segmentation in large-scale scenes. The
extensive experiments and comparisons demonstrate that our approach outperforms
the state-of-the-art methods on a variety of standard benchmark datasets (e.g.,
ModelNet10, ModelNet40, ShapeNet-part, S3DIS, and ScanNet).Comment: 17 pages, 14 figures. To appear, Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), June 201
A survey on Deep Learning Advances on Different 3D Data Representations
3D data is a valuable asset the computer vision filed as it provides rich
information about the full geometry of sensed objects and scenes. Recently,
with the availability of both large 3D datasets and computational power, it is
today possible to consider applying deep learning to learn specific tasks on 3D
data such as segmentation, recognition and correspondence. Depending on the
considered 3D data representation, different challenges may be foreseen in
using existent deep learning architectures. In this work, we provide a
comprehensive overview about various 3D data representations highlighting the
difference between Euclidean and non-Euclidean ones. We also discuss how Deep
Learning methods are applied on each representation, analyzing the challenges
to overcome.Comment: 35 page