| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - image-classification |
| - data-augmentation |
| - small-sample-learning |
| - empirical-study |
| - rethinking |
| datasets: |
| - cifar100 |
| - cifar10 |
| metrics: |
| - accuracy |
| --- |
| |
| # Rethinking Augmentation: Experiment Results |
| # 小样本增强策略再思考：实验结果库 |
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| Official experiment results repository for the paper: |
| **"When More is Not Better: Rethinking Data Augmentation under Small-Sample Regimes"** |
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| **TL;DR**: We find that in small-sample regimes (e.g., CIFAR-100 100-shot), complex augmentation strategies (like RandAugment) often yield diminishing returns and high instability. A single, well-tuned operation can achieve comparable accuracy with significantly lower variance. |
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| **核心发现**：在小样本场景下，盲目增加数据增强的复杂度（如 RandAugment）往往收益递减且带来极大的不稳定性。我们发现，单一且经过调优的增强操作能在保持精度的同时显著降低方差。 |
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| ## 📊 Key Findings / 核心发现 |
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| ### 1. Stability-Accuracy Trade-off / 稳定性与精度的权衡 (CIFAR-100 100-shot) |
| | Policy / 策略 | Val Acc (%) | Stability (Std) | Note | |
| | :--- | :--- | :--- | :--- | |
| | **RandAugment** (N=2,M=9) | 42.24% | 1.17 | High Variance (Unstable) | |
| | **Single-Op** (Ours) | 40.74% | **0.78** (Lowest) | **Stable & Reliable** | |
| | **Baseline** | 39.90% | 1.01 | - | |
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| ### 2. Zero-Variance Generalization / 零方差泛化 (CIFAR-10 50-shot) |
| | Metric / 指标 | Result / 结果 | |
| | :--- | :--- | |
| | **Top-1 Accuracy** | 50.0% | |
| | **Stability (Std)** | **0.0** (Zero Variance across 3 seeds) | |
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| --- |
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| ## 📂 Repository Structure / 仓库结构 |
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| - `checkpoints/`: PyTorch model weights (`best_model.pth`). / 官方模型权重。 |
| - `figures/`: Paper visualizations (Heatmaps, Trade-off plots). / 论文可视化图表（热图、权衡对比图等）。 |
| - [phase_c_final_policy.json](phase_c_final_policy.json): The discovered optimal single-operation policy. / 搜索出的最优单一增强策略。 |
| - [cifar10_50shot_results.csv](cifar10_50shot_results.csv): CIFAR-10 generalization experiment data. / CIFAR-10 泛化实验数据。 |
| - [stability_seeds_results.csv](stability_seeds_results.csv): Raw data verifying the stability claim. / 验证“稳定性”结论的原始数据。 |
| - [destructiveness_metrics.csv](destructiveness_metrics.csv): LPIPS/SSIM analysis for semantic preservation. / 语义保真度分析数据。 |
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| ## 📜 Citation / 引用 |
| If you find this study useful, please cite our work: |
| 如果您觉得这项研究对您有启发，请引用我们的工作： |
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| *(Citation will be updated upon acceptance / 论文接收后更新)* |
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| ## 🔗 Links / 相关链接 |
| - **Code & Paper**: [imnotnoahhh/Rethinking-Augmentation](https://github.com/imnotnoahhh/Rethinking-Augmentation) |
