progressive neural networks
**Progressive neural networks** is **a continual-learning architecture that adds new network columns for new tasks while preserving earlier parameters** - Each new task gets a fresh module with lateral connections to prior modules so old knowledge is reused without destructive overwriting.
**What Is Progressive neural networks?**
- **Definition**: A continual-learning architecture that adds new network columns for new tasks while preserving earlier parameters.
- **Core Mechanism**: Each new task gets a fresh module with lateral connections to prior modules so old knowledge is reused without destructive overwriting.
- **Operational Scope**: It is applied during data scheduling, parameter updates, or architecture design to preserve capability stability across many objectives.
- **Failure Modes**: Model growth can become expensive as many tasks are added and inference paths expand.
**Why Progressive neural networks Matters**
- **Retention and Stability**: It helps maintain previously learned behavior while new tasks are introduced.
- **Transfer Efficiency**: Strong design can amplify positive transfer and reduce duplicate learning across tasks.
- **Compute Use**: Better task orchestration improves return from fixed training budgets.
- **Risk Control**: Explicit monitoring reduces silent regressions in legacy capabilities.
- **Program Governance**: Structured methods provide auditable rules for updates and rollout decisions.
**How It Is Used in Practice**
- **Design Choice**: Select the method based on task relatedness, retention requirements, and latency constraints.
- **Calibration**: Choose column sizes and connection policies based on retention targets and long-run memory budgets.
- **Validation**: Track per-task gains, retention deltas, and interference metrics at every major checkpoint.
Progressive neural networks is **a core method in continual and multi-task model optimization** - It preserves prior capabilities while enabling controlled forward transfer.