brainstorming
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).
Skills 둘러보기
개발 및 엔지니어링 · SKILL.md
sar_reliable_entropy_filter
sar_reliable_entropy_filterUse this skill when implementing SAR reliable entropy minimization, entropy-margin filtering, or proxy checks for selected samples.
Independent GitHub snapshot · Automated classification · Original documentation governs
Skill 문서
GitHubname: sar_reliable_entropy_filter description: Use this skill when implementing SAR reliable entropy minimization, entropy-margin filtering, or proxy checks for selected samples.
Reliable Entropy Filter
When To Use
Use this skill for SAR stable test-time adaptation work that needs: Compute softmax entropy and select reliable samples below the SAR entropy margin. Do not use it as a generic ImageNet-C runner or as permission to read the original SAR repository during recovery.
Inputs
- logits, class count, optional margin
Outputs
- per-sample entropy values, selected indices, mean reliable entropy
Workflow
- Read the caller-provided stream, logits, parameter metadata, or entropy trace.
- Apply the SAR-specific invariant documented in this skill: Entropy is a practical proxy for excluding samples with large noisy gradients during online adaptation.
- Produce structured numeric evidence for recovery logs.
- Keep proxy/reduced results explicitly labeled when the full ImageNet-C runtime is unavailable.
Validation
Run python scripts/reliable_entropy_filter.py when the script has a CLI, then run python -m pytest tests or the Distiller skill validator with --run-tests.
Limitations
This skill captures a reusable SAR mechanism. It does not contain pretrained weights, ImageNet-C data, or original repository code, and it should be combined with other SAR skills for end-to-end recovery.