Information Competing Process for Learning Diversified Representations
This addresses the open problem of enriching information in feature representations for machine learning applications, though it appears incremental as it builds on existing representation learning methods.
The paper tackles the problem of learning diversified representations by proposing the Information Competing Process (ICP), which separates representations into two parts with mutual information constraints and forces them to compete, resulting in improved discriminative and disentangled representations for tasks like image classification and reconstruction.
Learning representations with diversified information remains as an open problem. Towards learning diversified representations, a new approach, termed Information Competing Process (ICP), is proposed in this paper. Aiming to enrich the information carried by feature representations, ICP separates a representation into two parts with different mutual information constraints. The separated parts are forced to accomplish the downstream task independently in a competitive environment which prevents the two parts from learning what each other learned for the downstream task. Such competing parts are then combined synergistically to complete the task. By fusing representation parts learned competitively under different conditions, ICP facilitates obtaining diversified representations which contain rich information. Experiments on image classification and image reconstruction tasks demonstrate the great potential of ICP to learn discriminative and disentangled representations in both supervised and self-supervised learning settings.