Class-Incremental Cloud-Device Collaborative Adaptation With Contrastive Learning in Dynamic Changing Environments

Lightweight models are often deployed on edge devices (e.g., smartphones and wearable devices) to enhance their scalability and practicability. To enhance their generalization ability in dynamic changing environments, cloud-device collaborative learning (CDCL) is proposed to transfer the generalization ability from large models on cloud servers to lightweight models deployed on devices. However, current methods mainly focus on solving the data distribution shifts for a limited number of seen classes but ignore the continual incoming new classes. Though existing class-incremental learning (CIL) methods achieve impressive performance, two major challenges arise when adapting them into the CDCL setting: 1) poor generalization of lightweight models during CIL and 2) overfitting during data-incremental learning. In this article, we explore a new problem named class-incremental CI-CDCL, and propose a contrastive prototypical network based CI-CDCL framework, aiming to improve the effectiveness of cloud-device collaboration in both class-incremental and data-incremental learning. Extensive experiments are conducted on two public datasets and the experimental results can evaluate the effectiveness of our proposed model.