Task Feature Extraction for Continual Learning Stability

The patent segments the model parameters into task-specific parameters and shared parameters. Task-specific parameters are updated during new task training while shared parameters are constrained using regularization. This segmentation allows the model to simultaneously maintain stability on old tasks through regularized shared parameters and achieve plasticity for new tasks through updated task-specific parameters, resolving the stability-plasticity tradeoff.

The patent introduces task embeddings as intermediary representations that bridge old and new tasks. These embeddings capture task-specific information while allowing the shared model parameters to remain stable. The task embeddings act as a mediator that enables adaptation to new tasks without requiring significant changes to the core model parameters, thus resolving the contradiction between stability and plasticity.

The patent segments the model into a fixed shared backbone and dynamic task-specific components. The shared backbone remains unchanged and computationally efficient, while task-specific parameters are only added or updated when needed for new tasks. This segmentation provides necessary model capacity for adaptation without permanently increasing computational resource requirements.

The patent implements dynamic task-specific parameters that are created or activated only when new tasks are introduced. These dynamic parameters allow the model to adapt to new tasks with increased capacity, but they do not permanently increase the model size or computational requirements for processing old tasks, thus resolving the contradiction between adaptability and resource usage.

The patent extracts task-specific information into separate task embeddings and task-specific parameters, separating it from the shared model parameters. This extraction allows the model to focus computational resources on learning task-specific patterns using smaller task-specific datasets, rather than requiring large datasets for instance-level learning of all tasks, thus reducing overall computational resource requirements while maintaining learning accuracy.

The patent creates a shared model backbone that serves multiple tasks universally. This shared backbone is trained once on aggregated task data and then reused across all tasks, eliminating the need for separate instance-level learning with large datasets for each individual task. The multi-functional shared parameters maintain learning accuracy while significantly reducing computational resource requirements.

The patent segments knowledge into task-specific parameters and shared parameters. Task-specific parameters are updated for each new task while shared parameters maintain general knowledge from all previous tasks. This segmentation prevents catastrophic forgetting because the shared parameters preserve general patterns across tasks, while task-specific parameters handle task-specific variations, allowing the system to accommodate an increasing number of tasks without forgetting.

The patent performs preliminary training of the shared model backbone on aggregated data from multiple tasks before fine-tuning on individual tasks. This preliminary action establishes a robust foundation of general knowledge that prevents forgetting when new tasks are introduced. The pre-trained shared parameters serve as a stable base that maintains reliability even as the number of tasks increases.