Counting with convolutional neural networks
Date
2021
Authors
Shastri, Viraj, author
Beveridge, J. Ross, advisor
Blanchard, Nathaniel, committee member
Peterson, Christopher, committee member
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Abstract
In this work, we tackle the question: Can neural networks count? More precisely, given an input image with a certain number of objects, can a neural network tell how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white triangles on a black background, oriented right-side up, down, left or right. We train a network to count the right-side up triangles; specifically, we see this as a closed-set classification problem where the class is the number of right-side up triangles in the image. These evaluations show that our networks, even in their simplest designs, are able to count a particular object in an image with a very small epsilon of approximation. We conclude that the neural networks are enforced with more complex learning capabilities than given credit for.
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Subject
convolutional neural network
visual learning
feature representations
closed-set counting