Compute and optimize the SR3 conditional denoising objective for a lightweight trainable denoiser. This reusable skill supports bounded SR3 recovery experiments.
Scanned 9/9/2026
Install to Claude Code
Skills Directorynpx -y skills add VectorSpaceLab/AREX-Skill --skill sr3_conditional_denoising_loss --agent claude-codeInstalls into .claude/skills of the current project.
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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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Designs system architecture, component specifications, and technical integration strategy. Use when: designing solutions, system architecture, technology stack, or integration approaches.
