The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Deep Neural Decision Forests
Abstract
We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner. To combine these two worlds, we introduce a stochastic and differentiable decision tree model, which steers the representation learning usually conducted in the initial layers of a (deep) convolutional network. Our model differs from conventional deep networks because a decision forest provides the final predictions and it differs from conventional decision forests since we propose a principled, joint and global optimization of split and leaf node parameters. We show experimental results on benchmark machine learning datasets like MNIST and ImageNet and find on-par or superior results when compared to state-of-the-art deep models. Most remarkably, we obtain Top5-Errors of only 7.84%/6.38% on ImageNet validation data when integrating our forests in a single-crop, single/seven model GoogLeNet architecture, respectively. Thus, even without any form of training data set augmentation we are improving on the 6.67% error obtained by the best GoogLeNet architecture (7 models, 144 crops).
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Cites
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Cited by
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Deep visual domain adaptation: A survey
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Tabular data: Deep learning is not all you need
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TabNet: Attentive Interpretable Tabular Learning
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Dynamic Neural Networks: A Survey
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A-ViT: Adaptive Tokens for Efficient Vision Transformer
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Neural Prototype Trees for Interpretable Fine-grained Image Recognition
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Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning
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GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
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Deep Differentiable Random Forests for Age Estimation
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AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests
Zhang, H., Bosch, J., Olsson, H. H., & Koppisetty, A. C. (2021) · 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa53835.2021.00047 · Cited by 10
Metadata
- Authors
- Kontschieder, P., Fiterau, M., Criminisi, A., & Bulo, S. R.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1467-1475
- OpenAlex
- W2220384803
- Cited by
- 490
- References
- 11
- FWCI
- 13.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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