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What Do Deep CNNs Learn About Objects?
lib:4a3b959214827302 (v1.0.0)
Authors: Xingchao Peng,Baochen Sun,Karim Ali,Kate Saenko
ArXiv: 1504.02485
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1504.02485v1
Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyse CNN representations, finding that, e.g., they are invariant to some 2D transformations Fischer et al. (2014), but are confused by particular types of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how invariant are CNNs to object-class variations caused by 3D shape, pose, and photorealism?
ArXiv: 1504.02485
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1504.02485v1
Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyse CNN representations, finding that, e.g., they are invariant to some 2D transformations Fischer et al. (2014), but are confused by particular types of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how invariant are CNNs to object-class variations caused by 3D shape, pose, and photorealism?
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