EWC Penalty

Use this skill when training a new task after estimating previous-task Fisher importance. It implements the paper equation lambda / 2 * sum(F_i * (theta_i - theta_star_i)^2) and the matching gradient.

Inputs

• Current parameters theta.
• Previous optimum theta_star.
• Diagonal Fisher vector fisher.
• EWC multiplier lambda_value.

Outputs

• Scalar EWC penalty.
• Gradient contribution to add to the new-task gradient.
• Optional summed penalty over multiple previous anchors.

Workflow

1. Validate matching vector dimensions and nonnegative Fisher entries.
2. Compute parameter deltas from the old optimum.
3. Apply the paper's lambda / 2 penalty scaling.
4. Add the gradient lambda * fisher * delta to the task-B gradient.
5. Sum penalties for multiple previous tasks when needed.

Validation

Run:

python tests/test_ewc_penalty.py

Limitations

This skill computes the consolidation term only. It must be combined with a task-specific loss in a training harness.