80 lines
3.1 KiB
Markdown
80 lines
3.1 KiB
Markdown
# QA Report — GNN-AMR Correction Presentation
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## PPTX Status
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- **File**: `output/final_presentation_cn.pptx`
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- **Creation**: ✅ Success
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- **Slides**: 17
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- **Dimensions**: 13.3" × 7.5" (16:9 widescreen)
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- **Images inserted**: 6 (from existing result files)
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## Slide Summary
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| Slide | Title | Type | Images |
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|-------|-------|------|--------|
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| 1 | GNN 引导的自适应网格加密 | Title | 0 |
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| 2 | 研究背景：为什么这个问题重要 | Background | 0 |
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| 3 | 技术瓶颈：传统残差驱动 AMR 的局限 | Gap | 0 |
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| 4 | 核心思路：GNN 学习修正信号 | Approach | 0 |
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| 5 | 全流程概览：5 步流水线 | Pipeline | 0 |
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| 6 | Step 1：数据生成与标记策略 | Method | 1 |
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| 7 | Step 1：15 维特征工程 | Method | 0 |
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| 8 | Step 3：CorrectionGNN 架构 | Architecture | 0 |
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| 9 | Step 3：训练结果 | Result | 0 |
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| 10 | Step 4a：离线评估 | Result | 1 |
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| 11 | Step 4b：Rollout 评估 | Result | 1 |
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| 12 | Step 5：端到端 AMR 可视化 | Visualization | 1 |
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| 13 | 单步对比：GNN vs Teacher vs Physics | Visualization | 1 |
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| 14 | 验证与稳健性 | Validation | 1 |
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| 15 | 创新点与可复用价值 | Innovation | 0 |
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| 16 | 局限性与未解决问题 | Limitations | 0 |
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| 17 | 总结 | Summary | 0 |
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## Figure Assets Used
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| Asset | Source | Slide |
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|-------|--------|-------|
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| `data_correction_check/sample0011_step002.png` | check_correction_data.py | 6 |
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| `result/correction/vis/sample0000_step004_vis.png` | test_correction.py | 10 |
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| `result/correction/rollout/aw_rel_vs_elements.png` | eval_correction.py | 11 |
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| `result/correction/viz/amr_overview.png` | viz_correction.py | 12 |
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| `result/correction/viz/marks_sample0005_step010.png` | viz_correction.py | 13 |
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| `result/correction/viz/compare_sample0005_step010.png` | viz_correction.py | 14 |
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## Data Sources
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- Training log: `outlook/ckpt/correction_train_log.json` (best epoch 40, AUC=0.950)
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- Rollout results: `outlook/result/correction/rollout/eval_results.json`
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- Dataset: `outlook/data_correction/` (100 samples, 1236 step files)
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## Verification
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- ✅ PPTX reopens successfully
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- ✅ 17 slides created
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- ✅ 6 images embedded
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- ✅ All slide titles in Chinese
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- ✅ Consistent color scheme (blue/red/green/orange accents)
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- ✅ 16:9 widescreen format
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- ⚠️ No speaker notes added (lightweight deck)
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- ⚠️ No headless renderer available for visual QA
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## Presentation Arc
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**Problem-to-Solution** (methods paper):
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1. Background: Helmholtz scattering + FEM
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2. Gap: Traditional AMR needs per-step FEM solve
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3. Approach: Correction learning (teacher - physics)
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4. Pipeline: 5-step workflow
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5. Architecture: CorrectionGNN
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6. Training: AUC=0.950, top-k 46.6%
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7. Evaluation: Offline + Rollout
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8. Visualization: AMR + step comparison
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9. Validation: 5-check data quality
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10. Innovation + Limitations + Summary
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## Known Limitations
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- No speaker notes (can be added manually)
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- Some text-heavy slides (features, architecture) use structured layouts rather than figures
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- Rollout improvement is modest (~2%) — presented honestly
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- No rendered slide previews (no headless renderer available)
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