Human–Machine Collaborative Learning for Streaming Data-Driven Scenarios

TL;DR

This paper introduces a framework where humans and machines collaborate to handle complex streaming data tasks, achieving better accuracy and robustness.

Contribution

A novel human–machine collaborative learning framework is proposed for streaming data tasks, combining human and machine strengths.

Findings

The framework achieved better accuracy in video anomaly detection, person re-identification, and sound event detection.

Human-machine collaboration improved robustness for hard sample recognition with minimal human intervention.

Experiments confirmed the framework's effectiveness in dynamic and untrustworthy conditions.

Abstract

Deep learning has been broadly applied in many fields and has greatly improved efficiency compared to traditional approaches. However, it cannot resolve issues well when there are a lack of training samples, or in some varying cases, it cannot give a clear output. Human beings and machines that work in a collaborative and equal mode to address complicated streaming data-driven tasks can achieve higher accuracy and clearer explanations. A novel framework is proposed which integrates human intelligence and machine intelligent computing, taking advantage of both strengths to work out complex tasks. Human beings are responsible for the highly decisive aspects of the task and provide empirical feedback to the model, whereas the machines undertake the repetitive computing aspects of the task. The framework will be executed in a flexible way through interactive human–machine cooperation mode, while it will be more robust for some hard samples recognition. We tested the framework using video anomaly detection, person re-identification, and sound event detection application scenarios, and we found that the human–machine collaborative learning mechanism obtained much better accuracy. After fusing human knowledge with deep learning processing, the final decision making is confirmed. In addition, we conducted abundant experiments to verify the effectiveness of the framework and obtained the competitive performance at the cost of a small amount of human intervention. The approach is a new form of machine learning, especially in dynamic and untrustworthy conditions.

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