Deep Incremental Boosting
This addresses the need for more efficient ensemble methods in deep learning, though it appears incremental in nature.
The paper tackles the problem of reducing training time and improving generalization in deep learning by introducing Deep Incremental Boosting, a technique adapted from AdaBoost, with preliminary results showing improvements on common datasets.
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.