令和元年度電気関係学会四国⽀部連合⼤会 講演論⽂集 (2019 新居浜⾼専)
2019 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (NIIHAMA)

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Eﬀect of Data Augmentation with Logistic Map
for Convolutional Neural Network Learning
Yuji YAMAUCHI

Yuichi MIYATA Yoko UWATE and Yoshifumi NISHIO( Tokushima University)

1. Introduction

CNN used for image recognition consists of input layers,
convolutional layers, pooling layers, fully connected layers
and output layers.

In recent years, the development of machine learning has
expanded its application to various fields. In the fashionindustry, the Electronic Commerce (EC) market is expanding. However, as the EC cannot add value, the used clothing
industry can not enter it. Therefore, a lot of data is necessary to make the Convolutional Neural Network (CNN)that is a neural network with many layers, showing excellent performance especially in the field of image recognition
learn, however there is little data about used clothes now.
We have methods of using data of good quality, data augmentation and transfer learning among other things when
making CNN learn in a small amount of data and performimage recognition.
In this study, we focus on the overfilling of data, and aim
to improve the learning accuracy and the test accuracy ofthe new overfilling method for small image data.

3. Simulation results
We define as the learning steps = 100. As the first dataset, 200 learning images are prepared for each of the 70, 80,
and 90’s T-shirts as a total of 600 original data set. We
create a data set consisting of 2,400 images, each with theoriginal image added to the augmented data using random
numbers, intermittent chaos, and pure chaos. We compare
the learning rates and test results obtained these four data
sets.
Figure 3 shows the learning accuracy and step on CNN.
Data sets that were augmented by pure chaos reached a
100% learning rate slightly faster than the other data sets.
Table 1 shows the CNN test accuracy learned for each data
set. Even in the test accuracy, the data set augmented bypure chaos showed the highest accuracy.

2. Proposed system
In this study, only rotation of the image is used to perform data augmentation. Therefore, the value obtained
from the logistic map is used as the angle of rotation of
the image. Among the logistic map, the values used werepure chaos and intermittent chaos, the values obtained fromthese two. Among the logistic maps, we used values obtained from pure chaos and intermittent chaos.
The logistic equation is described as follows:(1)
xn+1 = αxn (1 − xn ).
Figure 1 shows a logistic map, and Fig. 2 shows a timeseries diagram of pure chaos and intermittent chaos.

Figure 3: Learning accuracy.
Table 1: Test accuracy.
Original dataRandom
Intermittent chaos (α=3.83)
Pure chaos (α=4)

Figure 1: Logistic map.

Test accuracy (%)
48.8637
52.272751.1364
56.8182

4. Conclusion(a) Intermittent chaos(α=3.83)

In this study, we verified the eﬀect of data augmentation
using chaos in learning with a small amount of data. The
method using pure chaos showed better results in the bothlearning rate and test accuracy in the expansion of data by
image rotation.
As our future works, we try a method other rotation
for converting data when padding data. Further, we check
whether it is valid for data composed of diﬀerent types ofimages.

(b) Pure chaos(α=4.00)

Figure 2: Time series of intermittent and pure chaos.

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