Learning After Model Deployment

TL;DR

This paper introduces ALMD, a paradigm for models to detect and learn new classes after deployment, addressing challenges of dynamic environments with a novel method called PLDA for real-time OOD detection and incremental learning.

Contribution

The paper proposes ALMD, a new paradigm for autonomous, continuous learning after deployment, and introduces PLDA, a method for dynamic OOD detection and incremental class learning.

Findings

PLDA effectively detects novel classes in real-time.

PLDA enables incremental learning without retraining from scratch.

Empirical results demonstrate PLDA's superiority in dynamic environments.

Abstract

In classic supervised learning, once a model is deployed in an application, it is fixed. No updates will be made to it during the application. This is inappropriate for many dynamic and open environments, where unexpected samples from unseen classes may appear. In such an environment, the model should be able to detect these novel samples from unseen classes and learn them after they are labeled. We call this paradigm Autonomous Learning after Model Deployment (ALMD). The learning here is continuous and involves no human engineers. Labeling in this scenario is performed by human co-workers or other knowledgeable agents, which is similar to what humans do when they encounter an unfamiliar object and ask another person for its name. In ALMD, the detection of novel samples is dynamic and differs from traditional out-of-distribution (OOD) detection in that the set of in-distribution (ID) classes expands as new classes are learned during application, whereas ID classes is fixed in traditional OOD detection. Learning is also different from classic supervised learning because in ALMD, we learn the encountered new classes immediately and incrementally. It is difficult to retrain the model from scratch using all the past data from the ID classes and the novel samples from newly discovered classes, as this would be resource- and time-consuming. Apart from these two challenges, ALMD faces the data scarcity issue because instances of new classes often appear sporadically in real-life applications. To address these issues, we propose a novel method, PLDA, which performs dynamic OOD detection and incremental learning of new classes on the fly. Empirical evaluations will demonstrate the effectiveness of PLDA.