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We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Day S. B. Goldstone, R. L · 2013
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P Agrawal, D Stansbury, J Malik, and J L Gallant · 2014
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Human-level concept learning through probabilistic program induction
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Joint embeddings of shapes and images via cnn image purification
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Single-view to multi-view: Reconstructing unseen views with a convolutional network
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2015
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
D L K Yamins, H Hong, C F Cadieu, E A Solomon, D Seibert, and J J DiCarlo · 2014
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Unsupervised visual representation learning by context prediction
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What makes an object memorable?
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Multi-view 3d object retrieval with deep embedding network
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Deep neural networks as a computational model for human shape sensitivity
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Adapting deep network features to capture psychological representations
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Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge
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