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

• URL: http://arxiv.org/abs/2107.02943v1
• Date: Sat, 26 Jun 2021 03:37:40 GMT
• Title: Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
• Abstract summary: 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. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25%$ label proportions.
• Score: 16.171151152904617
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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