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

arXiv: https://arxiv.org/abs/2107.02943 · HTML: https://mlsanity.com/paper/2107.02943/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [DEVDAN: Deep Evolving Denoising Autoencoder](https://mlsanity.com/paper/1910.04062/) (2019)
- [Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments](https://mlsanity.com/paper/1810.07348/) (2018)
- [Automatic Construction of Multi-layer Perceptron Network from Streaming Examples](https://mlsanity.com/paper/1910.03437/) (2019)
- [Evolving Ensemble Fuzzy Classifier](https://mlsanity.com/paper/1705.06460/) (2017)
- [Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams](https://mlsanity.com/paper/1808.02234/) (2018)
- [An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams](https://mlsanity.com/paper/1808.08517/) (2018)
- [Online Tool Condition Monitoring Based on Parsimonious Ensemble+](https://mlsanity.com/paper/1711.01843/) (2017)
- [Weakly Supervised Deep Learning Approach in Streaming Environments](https://mlsanity.com/paper/1911.00847/) (2019)
- [PALM: An Incremental Construction of Hyperplanes for Data Stream Regression](https://mlsanity.com/paper/1805.04258/) (2018)
- [Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS](https://mlsanity.com/paper/1807.06996/) (2018)

## Built on by (most cited first, 2 of 2)

- [VGFlow: Visibility guided Flow Network for Human Reposing](https://mlsanity.com/paper/2211.08540/) (2022)
- [Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling](https://mlsanity.com/paper/2112.11019/) (2021)

## Abstract (arXiv)

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 (DA3) 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.