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
Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Tabular data: Deep learning is not all you need
Shwartz-Ziv, R., & Armon, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.011 · Cited by 2,172
TabNet: Attentive Interpretable Tabular Learning
Arik, S. Ö., & Pfister, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16826 · Cited by 1,771
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
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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- Kontschieder, P., Fiterau, M., Criminisi, A., & Bulo, S. R. (2015). Deep Neural Decision Forests. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.172
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