This document discusses using a convolutional neural network (CNN) for perceptual change recognition. It describes using a pre-trained VGG16 CNN model and fine-tuning the fully connected layers. The CNN is able to learn border-ownership representations from unlabeled data and can recognize changes between ambiguous figures with over 80% accuracy. Training takes around 158 simulation steps, with recognition performance improving over time.

1. Convolutional Neural Network
Recognition Model of Perceptual Change Using Convolutional Neural
Network
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[1]Rock, I., Hall, S., Davis, J..: Why do ambiguous figures reverse?, Acta Psychologica, Vol. 87, pp. 33– 59 (1994)

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• border-ownership
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[2] , : , D, Vol. 84, No. 7, pp. 1485–1494 (2001)
[3] , : , , Vol. 103, No. 732, pp. 117–122 (2004)

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Convolutional Neural Network

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• Convolutional Neural Network(CNN)
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• CNN VGG16
• full connect fine tuning
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512×7×7
[4]S. Lawrence and C. L. Giles and Ah Chung Tsoi and A. D. Back: Face Recognition: A Convolutional Neural-Network
Approach, IEEE Transactions on Neural Networks, Vol. 8, No. 1, pp. 98–113 (1997)
VGG16
(convolution , pooling )
VGG16
(full connect )
0.8 0.2

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512×1×1

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0.8 0.2
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512×7×7
0.9 0.1
512×1×1
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512×7×7 512×1×1
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time (simulation step) time (simulation step) time (simulation step)

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• zeiler [4]
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[5]Zeiler, Matthew D., Fergus, R.: Visualizing and un- derstanding convolutional networks, European Con- ference on
Computer Vision, pp. 818–833 (2014)

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Convolutional Neural Network
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