---
title: "Deep Neural Decision Forests"
authors:
  - "Peter Kontschieder"
  - "Madalina Fiterau"
  - "Antonio Criminisi"
  - "Samuel Rota Bulò"
doi: ""
url: "https://ijcai.org/proceedings/2016/628"
pdf: "https://ijcai.org/proceedings/2016/0628.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4190-4193"
track: ""
keywords:
---

# Deep Neural Decision Forests

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

*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.