Deep Incremental Boosting
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Abstract (arXiv)
This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep Incremental Boosting brings to traditional Ensemble methods in Deep Learning.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2017.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 1; 2024: 1.
7 papers.
- 2021Ensemble deep learning: A reviewcited by 283
- 2021Revisiting Locally Supervised Learning: an Alternative to End-to-end Trainingcited by 71
- 2021Efficient Diversity-Driven Ensemble for Deep Neural Networkscited by 11
- 2024End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Trainingcited by 8
- 2021Sparsely ensembled convolutional neural network classifiers via reinforcement learningcited by 2
- 2021To Boost or not to Boost: On the Limits of Boosted Neural Networkscited by 0
- 2023Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supplycited by 0
- 2024End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training14 Feb 2024
- 2023Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply12 Dec 2023
- 2021Efficient Diversity-Driven Ensemble for Deep Neural Networks26 Dec 2021
- 2021To Boost or not to Boost: On the Limits of Boosted Neural Networks28 Jul 2021
- 2021Ensemble deep learning: A review6 Apr 2021
- 2021Sparsely ensembled convolutional neural network classifiers via reinforcement learning7 Feb 2021
- 2021Revisiting Locally Supervised Learning: an Alternative to End-to-end Training26 Jan 2021