Training A Multi-stage Deep Classifier with Feedback Signals
Chao Xu Yu Yang Rongzhao Wang Guan Wang Bojia Lin
Multi-Stage Classifier (MSC) - several classifiers working sequentially in an
arranged order and classification decision is partially made at each step - is
widely used in industrial applications for various resource limitation reasons.
The classifiers of a multi-stage process are usually Neural Network (NN) models
trained independently or in their inference order without considering the
signals from the latter stages. Aimed at two-stage binary classification
process, the most common type of MSC, we propose a novel training framework,
named Feedback Training. The classifiers are trained in an order reverse to
their actual working order, and the classifier at the later stage is used to
guide the training of initial-stage classifier via a sample weighting method.
We experimentally show the efficacy of our proposed approach, and its great
superiority under the scenario of few-shot training.