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name: source_to_target_output_mapping description: Use this skill when converting frozen source classifier outputs into target-class probabilities through non-overlapping many-to-one label groups or a small trainable linear head. It is appropriate for model reprogramming recovery when source weights must remain unchanged.

source_to_target_output_mapping

When to use

Use this skill when converting frozen source classifier outputs into target-class probabilities through non-overlapping many-to-one label groups or a small trainable linear head. It is appropriate for model reprogramming recovery when source weights must remain unchanged.

Do not use this skill to fine-tune or inspect an original paper repository. It is for reusable model-reprogramming mechanisms derived from the paper.

Inputs

• Small target-domain samples or source-output vectors.
• Declared source dimensions/classes and target dimensions/classes.
• Reprogramming parameters, masks, mappings, or training traces as appropriate.

Outputs

• Deterministic transformed vectors, target probabilities, training traces, or mechanism-check dictionaries.
• Explicit metadata sufficient for downstream validation of source-model immutability and trainable-parameter scope.

Workflow

1. Validate dimensional assumptions from the model-reprogramming paper: target input dimension no larger than source input dimension and target label count no larger than source label count when using label mapping.
2. Apply only the local transformation, mapping, training, or checking operation owned by this skill.
3. Record numeric outputs and do not mutate frozen source model parameters.
4. In recovery, save traces so the experiment gate can verify optimizer steps and mechanism checks.

Validation

Run python ../../../../Paper2Skills-Agent/src/packages/paper2skills-agent/src/paper2skills/skills/module-to-skill/scripts/validate_skill_tree.py <skill_dir> --run-tests from a suitable workspace, or directly run the tests in tests/.

Limitations

These utilities are deliberately small and deterministic. They validate the mechanism, not full-scale ImageNet, speech, biomedical, or language-model experiments.