63,201 research outputs found
Many Task Learning with Task Routing
Typical multi-task learning (MTL) methods rely on architectural adjustments
and a large trainable parameter set to jointly optimize over several tasks.
However, when the number of tasks increases so do the complexity of the
architectural adjustments and resource requirements. In this paper, we
introduce a method which applies a conditional feature-wise transformation over
the convolutional activations that enables a model to successfully perform a
large number of tasks. To distinguish from regular MTL, we introduce Many Task
Learning (MaTL) as a special case of MTL where more than 20 tasks are performed
by a single model. Our method dubbed Task Routing (TR) is encapsulated in a
layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario
successfully fits hundreds of classification tasks in one model. We evaluate
our method on 5 datasets against strong baselines and state-of-the-art
approaches.Comment: 8 Pages, 5 Figures, 2 Table
Multitask Learning for Network Traffic Classification
Traffic classification has various applications in today's Internet, from
resource allocation, billing and QoS purposes in ISPs to firewall and malware
detection in clients. Classical machine learning algorithms and deep learning
models have been widely used to solve the traffic classification task. However,
training such models requires a large amount of labeled data. Labeling data is
often the most difficult and time-consuming process in building a classifier.
To solve this challenge, we reformulate the traffic classification into a
multi-task learning framework where bandwidth requirement and duration of a
flow are predicted along with the traffic class. The motivation of this
approach is twofold: First, bandwidth requirement and duration are useful in
many applications, including routing, resource allocation, and QoS
provisioning. Second, these two values can be obtained from each flow easily
without the need for human labeling or capturing flows in a controlled and
isolated environment. We show that with a large amount of easily obtainable
data samples for bandwidth and duration prediction tasks, and only a few data
samples for the traffic classification task, one can achieve high accuracy. We
conduct two experiment with ISCX and QUIC public datasets and show the efficacy
of our approach
Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification
Image classification is a challenging problem which aims to identify the
category of object in the image. In recent years, deep Convolutional Neural
Networks (CNNs) have been applied to handle this task, and impressive
improvement has been achieved. However, some research showed the output of CNNs
can be easily altered by adding relatively small perturbations to the input
image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are
proposed, which can help eliminating this limitation. Experiments on MNIST
dataset revealed that capsules can better characterize the features of object
than CNNs. But it's hard to find a suitable quantitative method to compare the
generalization ability of CNNs and CapsNets. In this paper, we propose a new
image classification task called Top-2 classification to evaluate the
generalization ability of CNNs and CapsNets. The models are trained on single
label image samples same as the traditional image classification task. But in
the test stage, we randomly concatenate two test image samples which contain
different labels, and then use the trained models to predict the top-2 labels
on the unseen newly-created two label image samples. This task can provide us
precise quantitative results to compare the generalization ability of CNNs and
CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism
among all capsules, it requires many parameters. To reduce the number of
parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules.
Experiments on five widely used benchmark image datasets demonstrate the method
significantly reduces the number of parameters, without losing the
effectiveness of extracting features. Further, on the Top-2 classification
task, the proposed PS CapsNets obtain impressive higher accuracy compared to
the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image
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