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sr3_conditional_denoising_loss

sr3_conditional_denoising_loss

Compute and optimize the SR3 conditional denoising objective for a lightweight trainable denoiser. This reusable skill supports bounded SR3 recovery experiments.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

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name: sr3_conditional_denoising_loss description: Compute and optimize the SR3 conditional denoising objective for a lightweight trainable denoiser. This reusable skill supports bounded SR3 recovery experiments.

sr3_conditional_denoising_loss

Use this skill for SR3 proxy recovery when implementing conditional_denoising_loss. Inputs are tiny numeric arrays or metadata; outputs are deterministic logs or metrics. Workflow follows SR3 paper equations for degradation, noising, denoising loss, reverse refinement, or evaluation as appropriate. Validate with validate_skill_tree.py --run-tests. Limitations: not a full SR3 U-Net implementation and not a substitute for FFHQ/ImageNet-scale training.

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