awesome-neural-trees
Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"
https://github.com/zju-vipa/awesome-neural-trees
Last synced: about 23 hours ago
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*News!*
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- [Paper - Guided Prototype Learning" [[Paper]](https://arxiv.org/pdf/2007.03047.pdf), two methods incorporating the class hierarchy into the regulariser of the general objective function in section 3. Although a regulariser is certainly feasible in utilize the class hierarchy, it is curiously rare in practice and these two methods are the only two examples we know of.
- [Paper - box model by distilling its knowledge into decision trees, belonging to section 2.2.
- [Paper - box model by distilling its knowledge into decision trees, belonging to section 2.2.
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A Taxonomy of Current Methods
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1. Non-hybrid: NNs and DTs Cooperated Approaches.
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- entropy net
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- [Paper - gates/GIRP)
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- [Paper - regularization-public)
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2. Semi-hybrid: NNs Leveraging Class Hierarchies.
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- [Paper - graph)
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- [Paper - Zheng/HD-CNN)
- [Paper - CV/Fine-Grained-or-Not)
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- [Paper - HMCNN)
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- [Paper - embeddings)
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3. Hybrid: Neural Decision Trees.
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- [Paper - pytorch-image-models)
- [Paper - decision-tree)
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- [Paper - Decision-Forests)
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- [Paper - Nauta/ProtoTree)
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- [Paper - NeT)
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- [Paper - paddle)
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