838 research outputs found
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Convolutional Neural Networks (CNN) are being increasingly used in computer
vision for a wide range of classification and recognition problems. However,
training these large networks demands high computational time and energy
requirements; hence, their energy-efficient implementation is of great
interest. In this work, we reduce the training complexity of CNNs by replacing
certain weight kernels of a CNN with Gabor filters. The convolutional layers
use the Gabor filters as fixed weight kernels, which extracts intrinsic
features, with regular trainable weight kernels. This combination creates a
balanced system that gives better training performance in terms of energy and
time, compared to the standalone CNN (without any Gabor kernels), in exchange
for tolerable accuracy degradation. We show that the accuracy degradation can
be mitigated by partially training the Gabor kernels, for a small fraction of
the total training cycles. We evaluated the proposed approach on 4 benchmark
applications. Simple tasks like face detection and character recognition (MNIST
and TiCH), were implemented using LeNet architecture. While a more complex task
of object recognition (CIFAR10) was implemented on a state of the art deep CNN
(Network in Network) architecture. The proposed approach yields 1.31-1.53x
improvement in training energy in comparison to conventional CNN
implementation. We also obtain improvement up to 1.4x in training time, up to
2.23x in storage requirements, and up to 2.2x in memory access energy. The
accuracy degradation suffered by the approximate implementations is within 0-3%
of the baseline.Comment: Accepted in ISLPED 201
A Compact CNN-Based Speech Enhancement With Adaptive Filter Design Using Gabor Function And Region-Aware Convolution
Speech enhancement (SE) is used in many applications, such as hearing devices, to improve speech intelligibility and quality. Convolutional neural network-based (CNN-based) SE algorithms in literature often employ generic convolutional filters that are not optimized for SE applications. This paper presents a CNN-based SE algorithm with an adaptive filter design (named ‘CNN-AFD’) using Gabor function and region-aware convolution. The proposed algorithm incorporates fixed Gabor functions into convolutional filters to model human auditory processing for improved denoising performance. The feature maps obtained from the Gabor-incorporated convolutional layers serve as learnable guided masks (tuned at backpropagation) for generating adaptive custom region-aware filters. The custom filters extract features from speech regions (i.e., ‘region-aware’) while maintaining translation-invariance. To reduce the high cost of inference of the CNN, skip convolution and activation analysis-wise pruning are explored. Employing skip convolution allowed the training time per epoch to be reduced by close to 40%. Pruning of neurons with high numbers of zero activations complements skip convolution and significantly reduces model parameters by more than 30%. The proposed CNN-AFD outperformed all four CNN-based SE baseline algorithms (i.e., a CNN-based SE employing generic filters, a CNN-based SE without region-aware convolution, a CNN-based SE trained with complex spectrograms and a CNN-based SE processing in the time-domain) with an average of 0.95, 1.82 and 0.82 in short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and logarithmic spectral distance (LSD) scores, respectively, when tasked to denoise speech contaminated with NOISEX-92 noises at −5, 0 and 5 dB signal-to-noise ratios (SNRs)
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Support Vector Machine classification of strong gravitational lenses
The imminent advent of very large-scale optical sky surveys, such as Euclid
and LSST, makes it important to find efficient ways of discovering rare objects
such as strong gravitational lens systems, where a background object is
multiply gravitationally imaged by a foreground mass. As well as finding the
lens systems, it is important to reject false positives due to intrinsic
structure in galaxies, and much work is in progress with machine learning
algorithms such as neural networks in order to achieve both these aims. We
present and discuss a Support Vector Machine (SVM) algorithm which makes use of
a Gabor filterbank in order to provide learning criteria for separation of
lenses and non-lenses, and demonstrate using blind challenges that under
certain circumstances it is a particularly efficient algorithm for rejecting
false positives. We compare the SVM engine with a large-scale human examination
of 100000 simulated lenses in a challenge dataset, and also apply the SVM
method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA
- …