11,187 research outputs found
Improving novelty detection with generative adversarial networks on hand gesture data
We propose a novel way of solving the issue of classification of
out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in
the Generative Adversarial Network (GAN) framework. A generative model augments
the data set in an online fashion with new samples and stochastic target
vectors, while a discriminative model determines the class of the samples. The
approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The
generative models performance was measured with a distance metric between
generated and real samples. The discriminative models were evaluated by their
accuracy on trained and novel classes. In terms of sample generation quality,
the GAN is significantly better than a random distribution (noise) in mean
distance, for all classes. In the classification tests, the baseline neural
network was not capable of identifying untrained gestures. When the proposed
methodology was implemented, we found that there is a trade-off between the
detection of trained and untrained gestures, with some trained samples being
mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or
90.2% (depending on the data set) was achieved with just 5% loss of accuracy on
trained classes
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Generative Adversarial Networks (GANs) have been used in many different
applications to generate realistic synthetic data. We introduce a novel GAN
with Autoencoder (GAN-AE) architecture to generate synthetic samples for
variable length, multi-feature sequence datasets. In this model, we develop a
GAN architecture with an additional autoencoder component, where recurrent
neural networks (RNNs) are used for each component of the model in order to
generate synthetic data to improve classification accuracy for a highly
imbalanced medical device dataset. In addition to the medical device dataset,
we also evaluate the GAN-AE performance on two additional datasets and
demonstrate the application of GAN-AE to a sequence-to-sequence task where both
synthetic sequence inputs and sequence outputs must be generated. To evaluate
the quality of the synthetic data, we train encoder-decoder models both with
and without the synthetic data and compare the classification model
performance. We show that a model trained with GAN-AE generated synthetic data
outperforms models trained with synthetic data generated both with standard
oversampling techniques such as SMOTE and Autoencoders as well as with state of
the art GAN-based models
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