11,878 research outputs found
DIRA: Dynamic Domain Incremental Regularised Adaptation
Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to
allow them to operate in complex, high-dimensional, non-linear, and dynamically
changing environments. Due to the complexity of these environments, DNN
classifiers may output misclassifications during operation when they face
domains not identified during development. Removing a system from operation for
retraining becomes impractical as the number of such AS increases. To increase
AS reliability and overcome this limitation, DNN classifiers need to have the
ability to adapt during operation when faced with different operational domains
using a few samples (e.g. 100 samples). However, retraining DNNs on a few
samples is known to cause catastrophic forgetting. In this paper, we introduce
Dynamic Incremental Regularised Adaptation (DIRA), a framework for operational
domain adaption of DNN classifiers using regularisation techniques to overcome
catastrophic forgetting and achieve adaptation when retraining using a few
samples of the target domain. Our approach shows improvements on different
image classification benchmarks aimed at evaluating robustness to distribution
shifts (e.g.CIFAR-10C/100C, ImageNet-C), and produces state-of-the-art
performance in comparison with other frameworks from the literature
Semantic labeling of places
Indoor environments can typically be divided into places with different
functionalities like corridors, kitchens, offices, or seminar rooms. We believe that
such semantic information enables a mobile robot to more efficiently accomplish a
variety of tasks such as human-robot interaction, path-planning, or localization. In
this paper, we propose an approach to classify places in indoor environments into
different categories. Our approach uses AdaBoost to boost simple features extracted from vision and laser range data. Furthermore,we apply a Hidden Markov Model to take spatial dependencies between robot poses into account and to increase the robustness of the classification. Our technique has been implemented and tested on real robots as well as in simulation. Experiments presented in this paper demonstrate that our approach can be utilized to robustly classify places into semantic categories
Feature extraction based on bio-inspired model for robust emotion recognition
Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de IngenierÃa y Ciencias HÃdricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de IngenierÃa y Ciencias HÃdricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de IngenierÃa y Ciencias HÃdricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin
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