346 research outputs found
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with
biosignal generative models based on generative adversarial networks (GANs),
which are a type of deep learning technique, was demonstrated in our previous
paper. GAN-based generative models only learn the projection between a random
distribution as input data and the distribution of training data.Therefore, the
relationship between input and generated data is unclear, and the
characteristics of the data generated from this model cannot be controlled.
This study proposes a method for generating time-series data based on GANs and
explores their ability to generate biosignals with certain classes and
characteristics. Moreover, in the proposed method, latent variables are
analyzed using canonical correlation analysis (CCA) to represent the
relationship between input and generated data as canonical loadings. Using
these loadings, we can control the characteristics of the data generated by the
proposed method. The influence of class labels on generated data is analyzed by
feeding the data interpolated between two class labels into the generator of
the proposed GANs. The CCA of the latent variables is shown to be an effective
method of controlling the generated data characteristics. We are able to model
the distribution of the time-series data without requiring domain-dependent
knowledge using the proposed method. Furthermore, it is possible to control the
characteristics of these data by analyzing the model trained using the proposed
method. To the best of our knowledge, this work is the first to generate
biosignals using GANs while controlling the characteristics of the generated
data
CNN training with graph-based sample preselection: application to handwritten character recognition
In this paper, we present a study on sample preselection in large training
data set for CNN-based classification. To do so, we structure the input data
set in a network representation, namely the Relative Neighbourhood Graph, and
then extract some vectors of interest. The proposed preselection method is
evaluated in the context of handwritten character recognition, by using two
data sets, up to several hundred thousands of images. It is shown that the
graph-based preselection can reduce the training data set without degrading the
recognition accuracy of a non pretrained CNN shallow model.Comment: Paper of 10 pages. Minor spelling corrections brought regarding the
v2. Accepted as an oral paper in the 13th IAPR Internationale Workshop on
Document Analysis Systems (DAS 2018
Scene Text Eraser
The character information in natural scene images contains various personal
information, such as telephone numbers, home addresses, etc. It is a high risk
of leakage the information if they are published. In this paper, we proposed a
scene text erasing method to properly hide the information via an inpainting
convolutional neural network (CNN) model. The input is a scene text image, and
the output is expected to be text erased image with all the character regions
filled up the colors of the surrounding background pixels. This work is
accomplished by a CNN model through convolution to deconvolution with
interconnection process. The training samples and the corresponding inpainting
images are considered as teaching signals for training. To evaluate the text
erasing performance, the output images are detected by a novel scene text
detection method. Subsequently, the same measurement on text detection is
utilized for testing the images in benchmark dataset ICDAR2013. Compared with
direct text detection way, the scene text erasing process demonstrates a
drastically decrease on the precision, recall and f-score. That proves the
effectiveness of proposed method for erasing the text in natural scene images
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels
Diffusion models have recently been used for medical image generation because
of their high image quality. In this study, we focus on generating medical
images with ordinal classes, which have ordinal relationships, such as severity
levels. We propose an Ordinal Diffusion Model (ODM) that controls the ordinal
relationships of the estimated noise images among the classes. Our model was
evaluated experimentally by generating retinal and endoscopic images of
multiple severity classes. ODM achieved higher performance than conventional
generative models by generating realistic images, especially in high-severity
classes with fewer training samples.Comment: Accepted at ISBI202
Local Style Awareness of Font Images
When we compare fonts, we often pay attention to styles of local parts, such
as serifs and curvatures. This paper proposes an attention mechanism to find
important local parts. The local parts with larger attention are then
considered important. The proposed mechanism can be trained in a
quasi-self-supervised manner that requires no manual annotation other than
knowing that a set of character images is from the same font, such as
Helvetica. After confirming that the trained attention mechanism can find
style-relevant local parts, we utilize the resulting attention for local
style-aware font generation. Specifically, we design a new reconstruction loss
function to put more weight on the local parts with larger attention for
generating character images with more accurate style realization. This loss
function has the merit of applicability to various font generation models. Our
experimental results show that the proposed loss function improves the quality
of generated character images by several few-shot font generation models.Comment: Accepted at ICDAR WML 202
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