# Deep Incremental Boosting

arXiv: https://arxiv.org/abs/1708.03704 · HTML: https://mlsanity.com/paper/1708.03704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 7 of 7)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Revisiting Locally Supervised Learning: an Alternative to End-to-end Training](https://mlsanity.com/paper/2101.10832/) (2021)
- [Efficient Diversity-Driven Ensemble for Deep Neural Networks](https://mlsanity.com/paper/2112.13316/) (2021)
- [End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training](https://mlsanity.com/paper/2402.09050/) (2024)
- [Sparsely ensembled convolutional neural network classifiers via reinforcement learning](https://mlsanity.com/paper/2102.03921/) (2021)
- [To Boost or not to Boost: On the Limits of Boosted Neural Networks](https://mlsanity.com/paper/2107.13600/) (2021)
- [Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply](https://mlsanity.com/paper/2312.07636/) (2023)

## 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.