Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams

TL;DR

This paper introduces WeScatterNet, a scalable semi-supervised learning framework for large-scale data streams that effectively handles label scarcity and concept drift using distributed computing and data augmentation techniques.

Contribution

WeScatterNet is a novel distributed semi-supervised network that addresses large-scale data streams with limited labels and concept drift, integrating model fusion and data augmentation.

Findings

Achieves high performance with only 25% labels.

Outperforms fully supervised methods on large-scale data streams.

Effective handling of concept drift and data scarcity.

Abstract

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.