Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning

By Deboleena Roy, Priyadarshini Panda, Kaushik Roy. 2018

29 citations recorded by the source corpus.

Subfields: Model Architectures, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

1. Peter Hase
2. Ting-Wu Chin
3. Diana Marculescu
4. Dimitrios Stamoulis
5. Junseok Kwon
6. Sung Woo Park
7. Kaushik Roy
8. Syed Shakib Sarwar

In Meta & Continual Learning

1. Kaushik Roy
2. Syed Shakib Sarwar
3. Aayush Ankit

Citations in the corpus

• Designing Adaptive Neural Networks for Energy-Constrained Image Classification (2018, background): “Finally, other works extend the cascading execution to more tree-like structures for image classification [18, 23].”
• Interpretable Image Recognition with Hierarchical Prototypes (2019, background): “See (Doshi-Velez and Kim 2017; Amodei et al. 2016; Rudin 2019) for a discussion of interpretability.”
• Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing (2017, background): “[27] where the network grows in a tree-like manner to accommodate the new classes.”
• 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions (2019, background): “Tree-structured Deep Networks: There have been several attempts to represent convolutional neural networks or long short-term memory using tree structures [5, 20, 27, 28, 31].”