34,048 research outputs found
Sparse Coding on Stereo Video for Object Detection
Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alleviate the need for large amounts of labeled data. We show that
replacing a typical supervised convolutional layer with an unsupervised
sparse-coding layer within a DCNN allows for better performance on a car
detection task when only a limited number of labeled training examples is
available. Furthermore, the network that incorporates sparse coding allows for
more consistent performance over varying initializations and ordering of
training examples when compared to a fully supervised DCNN. Finally, we compare
activations between the unsupervised sparse-coding layer and the supervised
convolutional layer, and show that the sparse representation exhibits an
encoding that is depth selective, whereas encodings from the convolutional
layer do not exhibit such selectivity. These result indicates promise for using
unsupervised sparse-coding approaches in real-world computer vision tasks in
domains with limited labeled training data
Building efficient deep Hebbian networks for image classification tasks
Multi-layer models of sparse coding (deep dictionary learning) and dimensionality
reduction (PCANet) have shown promise as unsupervised learning models for image classification tasks. However, the pure implementations of these models have limited generalisation capabilities and high computational cost. This work introduces the Deep Hebbian Network (DHN), which combines the advantages of sparse coding, dimensionality reduction, and convolutional neural networks for learning features from images. Unlike in other deep neural networks,
in this model, both the learning rules and neural architectures are derived from
cost-function minimizations. Moreover, the DHN model can be trained online due to its Hebbian components. Different configurations of the DHN have been tested on scene and image classification tasks. Experiments show that the DHN model can automatically discover highly discriminative features directly from
image pixels without using any data augmentation or semi-labeling
Sparse Coding with Structured Sparsity Priors and Multilayer Architecture for Image Classification
Applying sparse coding on large dataset for image classification is a long standing problem in the field of computer vision. It has been found that the sparse coding models exhibit disappointing performance on these large datasets where variability is broad and anomalies are common. Conversely, deep neural networks thrive on bountiful data. Their success has encouraged researchers to try and augment the learning capacity of traditionally shallow sparse coding methods by adding layers. Multilayer sparse coding networks are expected
to combine the best of both sparsity regularizations and deep architectures. To date, however, endeavors to marry the two techniques have not achieved significant improvements over their individual counterparts.
In this thesis, we first briefly review multiple structured sparsity priors as well as various supervised dictionary learning techniques with applications on hyperspectral image classification. Based on the structured sparsity priors and dictionary learning techniques, we then develop a novel multilayer sparse coding network that contains thirteen sparse coding layers. The proposed sparse coding network learns both the dictionaries and the regularization parameters simultaneously using an end-to-end supervised learning scheme. We show empirical evidence that the regularization parameters can adapt to the given training data. We also propose applying dimension reduction within sparse coding networks to dramatically reduce the output dimensionality of the sparse coding layers and mitigate computational costs. Moreover, our sparse coding network is compatible with other powerful deep learning techniques such as drop out, batch normalization and shortcut connections. Experimental results show that the proposed multilayer sparse coding network produces classification accuracy competitive with the deep neural networks while using significantly fewer parameters and layers
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, % FVC implementations employ the
Gaussian mixture model (GMM) to characterize the generation process of local
features. This choice has shown to be sufficient for traditional low
dimensional local features, e.g., SIFT; and typically, good performance can be
achieved with only a few hundred Gaussian distributions. However, the same
number of Gaussians is insufficient to model the feature space spanned by
higher dimensional local features, which have become popular recently. In order
to improve the modeling capacity for high dimensional features, it turns out to
be inefficient and computationally impractical to simply increase the number of
Gaussians. In this paper, we propose a model in which each local feature is
drawn from a Gaussian distribution whose mean vector is sampled from a
subspace. With certain approximation, this model can be converted to a sparse
coding procedure and the learning/inference problems can be readily solved by
standard sparse coding methods. By calculating the gradient vector of the
proposed model, we derive a new fisher vector encoding strategy, termed Sparse
Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently
developed Deep Convolutional Neural Network (CNN) descriptor as a high
dimensional local feature and implement image classification with the proposed
SCFVC. Our experimental evaluations demonstrate that our method not only
significantly outperforms the traditional GMM based Fisher vector encoding but
also achieves the state-of-the-art performance in generic object recognition,
indoor scene, and fine-grained image classification problems.Comment: Appearing in Proc. Advances in Neural Information Processing Systems
(NIPS) 2014, Montreal, Canad
A linear approach for sparse coding by a two-layer neural network
Many approaches to transform classification problems from non-linear to
linear by feature transformation have been recently presented in the
literature. These notably include sparse coding methods and deep neural
networks. However, many of these approaches require the repeated application of
a learning process upon the presentation of unseen data input vectors, or else
involve the use of large numbers of parameters and hyper-parameters, which must
be chosen through cross-validation, thus increasing running time dramatically.
In this paper, we propose and experimentally investigate a new approach for the
purpose of overcoming limitations of both kinds. The proposed approach makes
use of a linear auto-associative network (called SCNN) with just one hidden
layer. The combination of this architecture with a specific error function to
be minimized enables one to learn a linear encoder computing a sparse code
which turns out to be as similar as possible to the sparse coding that one
obtains by re-training the neural network. Importantly, the linearity of SCNN
and the choice of the error function allow one to achieve reduced running time
in the learning phase. The proposed architecture is evaluated on the basis of
two standard machine learning tasks. Its performances are compared with those
of recently proposed non-linear auto-associative neural networks. The overall
results suggest that linear encoders can be profitably used to obtain sparse
data representations in the context of machine learning problems, provided that
an appropriate error function is used during the learning phase
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
The availability of large-scale annotated image datasets and recent advances
in supervised deep learning methods enable the end-to-end derivation of
representative image features that can impact a variety of image analysis
problems. Such supervised approaches, however, are difficult to implement in
the medical domain where large volumes of labelled data are difficult to obtain
due to the complexity of manual annotation and inter- and intra-observer
variability in label assignment. We propose a new convolutional sparse kernel
network (CSKN), which is a hierarchical unsupervised feature learning framework
that addresses the challenge of learning representative visual features in
medical image analysis domains where there is a lack of annotated training
data. Our framework has three contributions: (i) We extend kernel learning to
identify and represent invariant features across image sub-patches in an
unsupervised manner. (ii) We initialise our kernel learning with a layer-wise
pre-training scheme that leverages the sparsity inherent in medical images to
extract initial discriminative features. (iii) We adapt a multi-scale spatial
pyramid pooling (SPP) framework to capture subtle geometric differences between
learned visual features. We evaluated our framework in medical image retrieval
and classification on three public datasets. Our results show that our CSKN had
better accuracy when compared to other conventional unsupervised methods and
comparable accuracy to methods that used state-of-the-art supervised
convolutional neural networks (CNNs). Our findings indicate that our
unsupervised CSKN provides an opportunity to leverage unannotated big data in
medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional
Sparse Kernel Network for Unsupervised Medical Image Analysis'). The
manuscript is available from following link
(https://doi.org/10.1016/j.media.2019.06.005
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