Deep Convolutional Inverse Graphics Network

@inproceedings{Kulkarni2015DeepCI,
  title={Deep Convolutional Inverse Graphics Network},
  author={Tejas D. Kulkarni and William F. Whitney and Pushmeet Kohli and Joshua B. Tenenbaum},
  booktitle={NIPS},
  year={2015}
}
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with respect to transformations such as out-of-plane rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm [11]. We propose a training procedure to encourage neurons in… CONTINUE READING
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