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Papers/Stacked What-Where Auto-encoders

Stacked What-Where Auto-encoders

Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann Lecun

2015-06-08Image ClassificationSemi-Supervised Image Classification
PaperPDFCodeCode

Abstract

We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervised, semi-supervised and unsupervised learning without relying on sampling during training. An instantiation of SWWAE uses a convolutional net (Convnet) (LeCun et al. (1998)) to encode the input, and employs a deconvolutional net (Deconvnet) (Zeiler et al. (2010)) to produce the reconstruction. The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet. Each pooling layer produces two sets of variables: the "what" which are fed to the next layer, and its complementary variable "where" that are fed to the corresponding layer in the generative decoder.

Results

TaskDatasetMetricValueModel
Image ClassificationCIFAR-10Percentage correct92.2SWWAE
Image ClassificationCIFAR-100Percentage correct69.1SWWAE
Image ClassificationMNISTPercentage error4.76Zhao et al. (2015) (auto-encoder)
Image ClassificationSTL-10Percentage correct74.3SWWAE
Image ClassificationSTL-10Percentage correct74.3SWWAE
Image ClassificationSTL-10, 1000 LabelsAccuracy74.3SWWAE
Semi-Supervised Image ClassificationSTL-10, 1000 LabelsAccuracy74.3SWWAE

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