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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
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source_to_target_output_mapping
source_to_target_output_mappingUse 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.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentación de la Skill
GitHubname: 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
- 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.
- Apply only the local transformation, mapping, training, or checking operation owned by this skill.
- Record numeric outputs and do not mutate frozen source model parameters.
- 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.