Papers › Stacked What-Where Auto-encoders
Stacked What-Where Auto-encoders
Junbo Zhao, Michael Mathieu, Ross Goroshin, Yann Lecun
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | SWWAE | Percentage correct | 92.2 | #183 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | SWWAE | Percentage correct | 69.1 | #178 of 211 | Archive leaderboard | report |
| Image Classification | MNIST | Zhao et al. (2015) (auto-encoder) | Percentage error | 4.76 | #62 of 81 | Archive leaderboard | report |
| Image Classification | STL-10 | SWWAE | Percentage correct | 74.3 | #79 of 117 | Archive leaderboard | report |
| Semi-Supervised Image Classification | STL-10, 1000 Labels | SWWAE | Accuracy | 74.30 | #13 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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