101,002 research outputs found
Segmented Learning for Class-of-Service Network Traffic Classification
Class-of-service (CoS) network traffic classification (NTC) classifies a
group of similar traffic applications. The CoS classification is advantageous
in resource scheduling for Internet service providers and avoids the necessity
of remodelling. Our goal is to find a robust, lightweight, and fast-converging
CoS classifier that uses fewer data in modelling and does not require
specialized tools in feature extraction. The commonality of statistical
features among the network flow segments motivates us to propose novel
segmented learning that includes essential vector representation and a
simple-segment method of classification. We represent the segmented traffic in
the vector form using the EVR. Then, the segmented traffic is modelled for
classification using random forest. Our solution's success relies on finding
the optimal segment size and a minimum number of segments required in
modelling. The solution is validated on multiple datasets for various CoS
services, including virtual reality (VR). Significant findings of the research
work are i) Synchronous services that require acknowledgment and request to
continue communication are classified with 99% accuracy, ii) Initial 1,000
packets in any session are good enough to model a CoS traffic for promising
results, and we therefore can quickly deploy a CoS classifier, and iii) Test
results remain consistent even when trained on one dataset and tested on a
different dataset. In summary, our solution is the first to propose
segmentation learning NTC that uses fewer features to classify most CoS traffic
with an accuracy of 99%. The implementation of our solution is available on
GitHub.Comment: The paper is accepted to be appeared in IEEE GLOBECOM 202
Discriminative Segmental Cascades for Feature-Rich Phone Recognition
Discriminative segmental models, such as segmental conditional random fields
(SCRFs) and segmental structured support vector machines (SSVMs), have had
success in speech recognition via both lattice rescoring and first-pass
decoding. However, such models suffer from slow decoding, hampering the use of
computationally expensive features, such as segment neural networks or other
high-order features. A typical solution is to use approximate decoding, either
by beam pruning in a single pass or by beam pruning to generate a lattice
followed by a second pass. In this work, we study discriminative segmental
models trained with a hinge loss (i.e., segmental structured SVMs). We show
that beam search is not suitable for learning rescoring models in this
approach, though it gives good approximate decoding performance when the model
is already well-trained. Instead, we consider an approach inspired by
structured prediction cascades, which use max-marginal pruning to generate
lattices. We obtain a high-accuracy phonetic recognition system with several
expensive feature types: a segment neural network, a second-order language
model, and second-order phone boundary features
Emergence of Object Segmentation in Perturbed Generative Models
We introduce a novel framework to build a model that can learn how to segment
objects from a collection of images without any human annotation. Our method
builds on the observation that the location of object segments can be perturbed
locally relative to a given background without affecting the realism of a
scene. Our approach is to first train a generative model of a layered scene.
The layered representation consists of a background image, a foreground image
and the mask of the foreground. A composite image is then obtained by
overlaying the masked foreground image onto the background. The generative
model is trained in an adversarial fashion against a discriminator, which
forces the generative model to produce realistic composite images. To force the
generator to learn a representation where the foreground layer corresponds to
an object, we perturb the output of the generative model by introducing a
random shift of both the foreground image and mask relative to the background.
Because the generator is unaware of the shift before computing its output, it
must produce layered representations that are realistic for any such random
perturbation. Finally, we learn to segment an image by defining an autoencoder
consisting of an encoder, which we train, and the pre-trained generator as the
decoder, which we freeze. The encoder maps an image to a feature vector, which
is fed as input to the generator to give a composite image matching the
original input image. Because the generator outputs an explicit layered
representation of the scene, the encoder learns to detect and segment objects.
We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS
2019), Spotlight presentatio
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothing based evaluation approach is employed to measure keyword
spotting performance. Our experimental results show that LSTM models trained
using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
trained baseline feed-forward Deep Neural Network (DNN). In addition,
max-pooling loss trained LSTM with randomly initialized network performs better
compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
trained LSTM initialized with a cross-entropy pre-trained network shows the
best performance, which yields relative reduction compared to baseline
feed-forward DNN in Area Under the Curve (AUC) measure
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