There has been much interest in unsuper- vised learning of hierarchical generative mod- els such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a dicult problem. To ad- dress this problem, we present the convolu- tional deep belief network, a hierarchical gen- erative model which scales to realistic image sizes. This model is translation-invariant and supports ecient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique which shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as ob- ject parts, from unlabeled images of objects and natural scenes. We demonstrate excel- lent performance on several visual recogni- tion tasks and show that our model can per- form hierarchical (bottom-up and top-down) inference over full-sized images