Proceedings

Deep Neural Decision Forests

Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulò

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 4190-4193.

Abstract

We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree model, which steers the formation of latent representations within the hidden layers of a deep network. The proposed model differs from conventional deep networks in that a decision forest provides the final predictions and it differs from conventional decision forests by introducing a principled, joint and global optimization of split and leaf node parameters. Our approach compares favourably to other state-of-the-art deep models on a large-scale image classification task like ImageNet.

Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulò. 2016. Deep Neural Decision Forests. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 4190–4193. https://ijcai.org/proceedings/2016/628
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