598 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
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Machine Learning with a Reject Option: A survey
Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with rejection recently gained interest. This
machine learning subfield enables machine learning models to abstain from
making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with rejection.
We introduce the conditions leading to two types of rejection, ambiguity and
novelty rejection, which we carefully formalize. Moreover, we review and
categorize strategies to evaluate a model's predictive and rejective quality.
Additionally, we define the existing architectures for models with rejection
and describe the standard techniques for learning such models. Finally, we
provide examples of relevant application domains and show how machine learning
with rejection relates to other machine learning research areas
Techniques and Approaches of Facial Recognition under Occlusion: A Review
A human face is one of the most prominent features used in the process of authenticating technical applications in the domains of security, biometrics, surveillance and forensics. Recognition and detection of facial features has thus become challenging due to problems of occlusion, emotion, image resolution, varying facial expressions and aging. Such attributes tend to have a great impact on the overall performance of a robust facial recognition system. Hence, facial recognition with presence of occlusion triggers to be a hindrance in the natural environment and thereby limits the system model to recognise faces. For this purpose, multiple research authors have inhibited strategies and techniques to address the issues of occlusion. Numerous developments in the field of machine learning and deep learning have constantly evolved with complex architectures that could design the model from scratch and perform image processing to attain maximum efficiency. Such approaches have the potential to accomplish highest state-of-the art accuracy by minimizing error loss. Nevertheless, facial recognition that tends to bypass occlusion is still imperative to limitations for real?world applications. Hence in this review paper, the authors highlight various problems that a facial recognition system with occlusion might face and thereby proposes to analyse various methods of recognition in order to cope with the existing problems. The paper also focuses on extraction approaches thus used present the novelty. The review finally ends, with a mention of future challenges with regards to occluded facial recognition
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