Between-class Learning for Image Classification

Understand

In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning).

• We generate between-class images by mixing two images belonging to different classes with a random ratio.
• We then input the mixed image to the model and train the model to output the mixing ratio.
• BC learning has the ability to impose constraints on the shape of the feature distributions, and thus the generalization ability is improved.