Shastri, Viraj, authorBeveridge, J. Ross, advisorBlanchard, Nathaniel, committee memberPeterson, Christopher, committee member2021-09-062021-09-062021https://hdl.handle.net/10217/233687In 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.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.convolutional neural networkvisual learningfeature representationsclosed-set countingText