2,455 research outputs found
Asynchronous dual-pipeline deep learning framework for online data stream classification
Data streaming classification has become an essential task in many fields where real-time decisions have to be made
based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their
incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity
streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness
of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep
learning framework in which training and testing are performed simultaneously in two different processes. The data stream
entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning
model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets
from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods
such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in
performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-
On the performance of deep learning models for time series classification in streaming
Processing data streams arriving at high speed requires the development of
models that can provide fast and accurate predictions. Although deep neural
networks are the state-of-the-art for many machine learning tasks, their
performance in real-time data streaming scenarios is a research area that has
not yet been fully addressed. Nevertheless, there have been recent efforts to
adapt complex deep learning models for streaming tasks by reducing their
processing rate. The design of the asynchronous dual-pipeline deep learning
framework allows to predict over incoming instances and update the model
simultaneously using two separate layers. The aim of this work is to assess the
performance of different types of deep architectures for data streaming
classification using this framework. We evaluate models such as multi-layer
perceptrons, recurrent, convolutional and temporal convolutional neural
networks over several time-series datasets that are simulated as streams. The
obtained results indicate that convolutional architectures achieve a higher
performance in terms of accuracy and efficiency.Comment: Paper submitted to the 15th International Conference on Soft
Computing Models in Industrial and Environmental Applications (SOCO 2020
Data streams classification using deep learning under different speeds and drifts
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate
predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in
real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has
been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The
design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the
model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL
architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons,
recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as
streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in
evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms
of accuracy and efficiency, but are also the most sensitive to concept drifts.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C22Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277
On the performance of deep learning models for time series classification in streaming
Processing data streams arriving at high speed requires the
development of models that can provide fast and accurate predictions.
Although deep neural networks are the state-of-the-art for many machine
learning tasks, their performance in real-time data streaming scenarios
is a research area that has not yet been fully addressed. Nevertheless,
there have been recent efforts to adapt complex deep learning models
for streaming tasks by reducing their processing rate. The design of the
asynchronous dual-pipeline deep learning framework allows to predict
over incoming instances and update the model simultaneously using two
separate layers. The aim of this work is to assess the performance of different
types of deep architectures for data streaming classification using
this framework. We evaluate models such as multi-layer perceptrons, recurrent,
convolutional and temporal convolutional neural networks over
several time-series datasets that are simulated as streams. The obtained
results indicate that convolutional architectures achieve a higher performance
in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-RJunta de Andalucía US-1263341Junta de Andalucía P18-RT-277
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201
Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification
Deep-learning is a cutting edge theory that is being applied to many fields.
For vision applications the Convolutional Neural Networks (CNN) are demanding
significant accuracy for classification tasks. Numerous hardware accelerators
have populated during the last years to improve CPU or GPU based solutions.
This technology is commonly prototyped and tested over FPGAs before being
considered for ASIC fabrication for mass production. The use of commercial
typical cameras (30fps) limits the capabilities of these systems for high speed
applications. The use of dynamic vision sensors (DVS) that emulate the behavior
of a biological retina is taking an incremental importance to improve this
applications due to its nature, where the information is represented by a
continuous stream of spikes and the frames to be processed by the CNN are
constructed collecting a fixed number of these spikes (called events). The
faster an object is, the more events are produced by DVS, so the higher is the
equivalent frame rate. Therefore, these DVS utilization allows to compute a
frame at the maximum speed a CNN accelerator can offer. In this paper we
present a VHDL/HLS description of a pipelined design for FPGA able to collect
events from an Address-Event-Representation (AER) DVS retina to obtain a
normalized histogram to be used by a particular CNN accelerator, called
NullHop. VHDL is used to describe the circuit, and HLS for computation blocks,
which are used to perform the normalization of a frame needed for the CNN.
Results outperform previous implementations of frames collection and
normalization using ARM processors running at 800MHz on a Zynq7100 in both
latency and power consumption. A measured 67% speedup factor is presented for a
Roshambo CNN real-time experiment running at 160fps peak rate.Comment: 7 page
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
- …