2,169 research outputs found
Image Denoising with Graph-Convolutional Neural Networks
Recovering an image from a noisy observation is a key problem in signal
processing. Recently, it has been shown that data-driven approaches employing
convolutional neural networks can outperform classical model-based techniques,
because they can capture more powerful and discriminative features. However,
since these methods are based on convolutional operations, they are only
capable of exploiting local similarities without taking into account non-local
self-similarities. In this paper we propose a convolutional neural network that
employs graph-convolutional layers in order to exploit both local and non-local
similarities. The graph-convolutional layers dynamically construct
neighborhoods in the feature space to detect latent correlations in the feature
maps produced by the hidden layers. The experimental results show that the
proposed architecture outperforms classical convolutional neural networks for
the denoising task.Comment: IEEE International Conference on Image Processing (ICIP) 201
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors
Tactile sensors provide useful contact data during the interaction with an
object which can be used to accurately learn to determine the stability of a
grasp. Most of the works in the literature represented tactile readings as
plain feature vectors or matrix-like tactile images, using them to train
machine learning models. In this work, we explore an alternative way of
exploiting tactile information to predict grasp stability by leveraging
graph-like representations of tactile data, which preserve the actual spatial
arrangement of the sensor's taxels and their locality. In experimentation, we
trained a Graph Neural Network to binary classify grasps as stable or slippery
ones. To train such network and prove its predictive capabilities for the
problem at hand, we captured a novel dataset of approximately 5000
three-fingered grasps across 41 objects for training and 1000 grasps with 10
unknown objects for testing. Our experiments prove that this novel approach can
be effectively used to predict grasp stability
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep
learning model able to predict structured sequences of data. Precisely, GCRN is
a generalization of classical recurrent neural networks (RNN) to data
structured by an arbitrary graph. Such structured sequences can represent
series of frames in videos, spatio-temporal measurements on a network of
sensors, or random walks on a vocabulary graph for natural language modeling.
The proposed model combines convolutional neural networks (CNN) on graphs to
identify spatial structures and RNN to find dynamic patterns. We study two
possible architectures of GCRN, and apply the models to two practical problems:
predicting moving MNIST data, and modeling natural language with the Penn
Treebank dataset. Experiments show that exploiting simultaneously graph spatial
and dynamic information about data can improve both precision and learning
speed
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