We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides an unified approach to supervised, semi-supervised and unsupervised learning without requiring sampling. An instantiation of SWWAE is essentially a convolutional net (Convnet) [14] coupled with a deconvolutional net (Deconvnet) [30] . The objective function includes reconstruction terms that penalize the hidden states in the Deconvnet for being different from the hidden state of the Convnet. Each pooling layer is seen producing two sets of variables: the "what" which are fed to the next layer, and the "where" (the max-pooling switch positions) that are fed to the corresponding layer in the generative decoder.
In REF , the authors proposed 'stacked what-where auto-encoders' (SWWAE), which consists of a feed-forward ConvNet (encoder), coupled with a feed-back DeConvnet (decoder).
Stacked What-Where Auto-encoders
ArXiv
ArXiv
Mathematics Computer Science