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Camera clustering for scalable stream-based active distillation
We present a scalable framework designed to craft efficient lightweight models for video object detection utilizing self-training and knowledge distillation techniques.
Active learning for data streams: a survey
However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream.
Robust online active learning
In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment.
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders
In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query.
Stream-based active learning with linear models
The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time.
