Adapting to Intra-Class Variations using Incremental Retraining with Exploratory Sampling

Abstract

Variations in appearance can detrimentally impact the accuracy of object detectors leading to an unacceptably high rate of missed detections. We propose an incremental retraining method that combines a self-training strategy with an uncertainty-based model for active learning. This enables us to augment an existing training set with selectively-labeled instances from a larger pool of examples that exhibit significant intra-class variation while minimizing the user’s labeling effort. Experimental results on an aerial imagery task demonstrate that the proposed method significantly improves over conventional passive learning techniques. Although the experiments presented in this paper are in the domain area of visual object recognition, our method is completely general and is applicable to a broad category of problems in machine learning.