Uncertainty-Aware Model Retraining for Online User Behavior Detection

The system performs automatic self-retraining by detecting uncertain behavior instances, collecting relevant video data, generating synthetic labeled data through behavior synthesis, and updating the model without external expert intervention. This self-service mechanism resolves the contradiction by eliminating the time-consuming manual expert labeling process while maintaining model performance improvement.

The system proactively identifies uncertain detection results and collects training data in advance before performance degradation becomes critical. By performing preliminary data collection and synthesis when uncertainty thresholds are exceeded, the system prepares training materials ahead of time, reducing the overall retraining cycle time and maintaining continuous model improvement.

The system eliminates the need for user data labeling by automatically detecting uncertain instances, collecting video data, and synthesizing labeled training data through behavior synthesis modules. This self-service approach maintains environment adaptability while completely removing the operational burden from users, resolving the contradiction between adaptation and ease of operation.

The system introduces an intermediate behavior synthesis module that acts as a mediator between raw video data and the training model. This intermediary automatically generates synthetic labeled data without requiring direct user interaction, thereby maintaining adaptability while simplifying user operation to minimal or no involvement.

The system introduces a local behavior synthesis module as an intermediary that generates synthetic labeled data within the user's own environment without transmitting sensitive information externally. This intermediary produces sufficient training data locally, maintaining data quantity needs while eliminating data security risks associated with crowd sourcing and external data transmission.

The system generates training data with local quality characteristics specific to each user's environment by synthesizing behaviors from locally collected video data. This local data generation approach provides sufficient quantity of labeled data tailored to specific environments while keeping all processing local, thereby eliminating data security risks of external transmission.

The system performs partial retraining by selectively updating only the necessary model components using synthetic data generated from uncertain detection instances. Instead of complete frequent retraining, this partial action approach maintains detection accuracy while reducing computational resource consumption and time requirements.

The system prepares synthetic training data in advance when uncertainty is detected, so that retraining can be performed efficiently with pre-prepared data. This preliminary data synthesis action reduces the computational burden during actual retraining operations, balancing detection accuracy maintenance with reduced resource consumption.