167,505 research outputs found
Online Deep Metric Learning
Metric learning learns a metric function from training data to calculate the
similarity or distance between samples. From the perspective of feature
learning, metric learning essentially learns a new feature space by feature
transformation (e.g., Mahalanobis distance metric). However, traditional metric
learning algorithms are shallow, which just learn one metric space (feature
transformation). Can we further learn a better metric space from the learnt
metric space? In other words, can we learn metric progressively and nonlinearly
like deep learning by just using the existing metric learning algorithms? To
this end, we present a hierarchical metric learning scheme and implement an
online deep metric learning framework, namely ODML. Specifically, we take one
online metric learning algorithm as a metric layer, followed by a nonlinear
layer (i.e., ReLU), and then stack these layers modelled after the deep
learning. The proposed ODML enjoys some nice properties, indeed can learn
metric progressively and performs superiorly on some datasets. Various
experiments with different settings have been conducted to verify these
properties of the proposed ODML.Comment: 9 page
DEVELOPMENT OF THE METHOD OF UNSUPERVISED TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BASED ON NEURAL GAS MODIFICATION
oai:ojs.localhost:article/469Technologies for computer analysis of visual information based on convolutional neural networks have been widely used, but there is still a shortage of working algorithms for continuous unsupervised training and re-training of neural networks in real time, limiting the effectiveness of their functioning under conditions of nonstationarity and a priori uncertainty. In addition, the back propagation method for learning multi-layer neural networks requires significant computational resources and the amount of marked learning data, which makes it difficult to implement them in autonomous systems with limited resources. One approach to reducing the computational complexity of deep machine learning and overfitting is use of the neural gas principles to implement learning in the process of direct information propagation and sparse coding to increase the compactness and informativeness of feature representation.
The paper considers the use of sparse coding neural gas for learning ten layers of the VGG-16 neural network on selective data from the ImageNet database. At the same time, it is suggested that the evaluation of the effectiveness of the feature extractor learning be carried out according to the results of so-called information-extreme machine learning with the teacher of the output classifier. Information-extreme learning is based on the principles of population optimization methods for binary coding of observations and the construction of radial-basic decision rules optimal in the information criterion in the binary Hamming space.
According to the results of physical modeling, it is shown that learning without a teacher ensures the accuracy of decision rules to 96.4 %, which is inferior to the accuracy of learning with the teacher, which is equal to 98.7 %. However, the absence of an error in the training algorithm for the backward propagation of the error causes the prospect of further research towards the development of meta-optimization algorithms to refine the feature extractor's filters and parameters of the unsupervised training algorith
A difference boosting neural network for automated star-galaxy classification
In this paper we describe the use of a new artificial neural network, called
the difference boosting neural network (DBNN), for automated classification
problems in astronomical data analysis. We illustrate the capabilities of the
network by applying it to star galaxy classification using recently released,
deep imaging data. We have compared our results with classification made by the
widely used Source Extractor (SExtractor) package. We show that while the
performance of the DBNN in star-galaxy classification is comparable to that of
SExtractor, it has the advantage of significantly higher speed and flexibility
during training as well as classification.Comment: 9 pages, 1figure, 7 tables, accepted for publication in Astronomy and
Astrophysic
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