10,081 research outputs found
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Transfer learning is a machine learning technique designed to improve
generalization performance by using pre-trained parameters obtained from other
learning tasks. For image recognition tasks, many previous studies have
reported that, when transfer learning is applied to deep neural networks,
performance improves, despite having limited training data. This paper proposes
a two-stage feature transfer learning method focusing on the recognition of
textural medical images. During the proposed method, a model is successively
trained with massive amounts of natural images, some textural images, and the
target images. We applied this method to the classification task of textural
X-ray computed tomography images of diffuse lung diseases. In our experiment,
the two-stage feature transfer achieves the best performance compared to a
from-scratch learning and a conventional single-stage feature transfer. We also
investigated the robustness of the target dataset, based on size. Two-stage
feature transfer shows better robustness than the other two learning methods.
Moreover, we analyzed the feature representations obtained from DLDs imagery
inputs for each feature transfer models using a visualization method. We showed
that the two-stage feature transfer obtains both edge and textural features of
DLDs, which does not occur in conventional single-stage feature transfer
models.Comment: Preprint of the journal article to be published in IPSJ TOM-51.
Notice for the use of this material The copyright of this material is
retained by the Information Processing Society of Japan (IPSJ). This material
is published on this web site with the agreement of the author (s) and the
IPS
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
The domain adaptation of satellite images has recently gained an increasing
attention to overcome the limited generalization abilities of machine learning
models when segmenting large-scale satellite images. Most of the existing
approaches seek for adapting the model from one domain to another. However,
such single-source and single-target setting prevents the methods from being
scalable solutions, since nowadays multiple source and target domains having
different data distributions are usually available. Besides, the continuous
proliferation of satellite images necessitates the classifiers to adapt to
continuously increasing data. We propose a novel approach, coined DAugNet, for
unsupervised, multi-source, multi-target, and life-long domain adaptation of
satellite images. It consists of a classifier and a data augmentor. The data
augmentor, which is a shallow network, is able to perform style transfer
between multiple satellite images in an unsupervised manner, even when new data
are added over the time. In each training iteration, it provides the classifier
with diversified data, which makes the classifier robust to large data
distribution difference between the domains. Our extensive experiments prove
that DAugNet significantly better generalizes to new geographic locations than
the existing approaches
Audio style transfer
'Style transfer' among images has recently emerged as a very active research
topic, fuelled by the power of convolution neural networks (CNNs), and has
become fast a very popular technology in social media. This paper investigates
the analogous problem in the audio domain: How to transfer the style of a
reference audio signal to a target audio content? We propose a flexible
framework for the task, which uses a sound texture model to extract statistics
characterizing the reference audio style, followed by an optimization-based
audio texture synthesis to modify the target content. In contrast to mainstream
optimization-based visual transfer method, the proposed process is initialized
by the target content instead of random noise and the optimized loss is only
about texture, not structure. These differences proved key for audio style
transfer in our experiments. In order to extract features of interest, we
investigate different architectures, whether pre-trained on other tasks, as
done in image style transfer, or engineered based on the human auditory system.
Experimental results on different types of audio signal confirm the potential
of the proposed approach.Comment: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Apr 2018, Calgary, France. IEE
Adversarial Inpainting of Medical Image Modalities
Numerous factors could lead to partial deteriorations of medical images. For
example, metallic implants will lead to localized perturbations in MRI scans.
This will affect further post-processing tasks such as attenuation correction
in PET/MRI or radiation therapy planning. In this work, we propose the
inpainting of medical images via Generative Adversarial Networks (GANs). The
proposed framework incorporates two patch-based discriminator networks with
additional style and perceptual losses for the inpainting of missing
information in realistically detailed and contextually consistent manner. The
proposed framework outperformed other natural image inpainting techniques both
qualitatively and quantitatively on two different medical modalities.Comment: To be submitted to ICASSP 201
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