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README.md

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+---
+license: mit
+task_categories:
+- image-classification
+- tabular-regression
+- reinforcement-learning
+tags:
+- active-learning
+- bayesian-deep-learning
+- out-of-distribution
+- uncertainty-estimation
+pretty_name: DCPV Experimental Artifacts
+size_categories:
+- 100M<n<1G
+---
+
+# DCPV: Density-Clustered / OOD-Aware Predictive Variance Artifacts
+
+This repository contains the experimental artifacts, results, and datasets for the paper **"Mitigating the Density Paradox in Bayesian Active Learning"** (DCPV).
+
+These artifacts support the reproducibility of our findings, demonstrating that standard Bayesian Active Learning methods (like Predictive Variance) often fail in Out-of-Distribution (OOD) settings, and that our proposed method (**Robust PV**) effectively mitigates this issue.
+
+## Repository Structure
+
+The repository is organized into three main directories:
+
+### 1. `results/` (Experimental Metrics)
+Contains raw `.npy` files recording the performance metrics for every round of Active Learning (up to 50 rounds).
+- **Structure**: `results/{metric}_{method}.npy`
+- **Key Metrics**:
+  - `ood_rate_*.npy`: Percentage of selected samples that were OOD (Lower is better).
+  - `rmse_*.npy`: Root Mean Squared Error on the Test Set (Lower is better).
+- **Methods**:
+  - `random`: Random Sampling (Baseline).
+  - `predictive_variance`: Standard PV (Fails in OOD).
+  - `robust_pv`: Our proposed method (Weighted by Mahalanobis density).
+  - `robust_pv_gmm`: Our proposed method (Weighted by GMM density).
+
+### 2. `plots/` (Visualizations)
+Generated figures illustrating the "Density Paradox" and the effectiveness of our solution.
+- **Key Figures**:
+  - `ood_composite_2x4.png`: Comprehensive view of OOD selection rates and RMSE across all datasets (MNIST, Fashion-MNIST, CIFAR-10, SVHN).
+  - `summary_tradeoff_v2.png`: Trade-off analysis between OOD robustness and ID performance.
+
+### 3. `data/` (Source Datasets)
+Contains the processed datasets used in the experiments to ensure exact reproducibility of splits and preprocessing.
+- **Datasets**:
+  - `CIFAR-10` / `CIFAR-100` (Python version)
+  - `SVHN` (Format 2, 32x32)
+  - `MNIST` / `Fashion-MNIST` (Raw binaries)
+
+## Usage
+
+You can download these artifacts directly or use the `huggingface_hub` library.
+
+### Option 1: Download via Python
+```python
+from huggingface_hub import snapshot_download
+
+# Download the entire repository
+path = snapshot_download(repo_id="darcook/DCPV-Artifacts", repo_type="dataset")
+print(f"Artifacts downloaded to: {path}")
+```
+
+### Option 2: Load Results with `datasets`
+(Example for listing files)
+```python
+from huggingface_hub import list_repo_files
+
+files = list_repo_files(repo_id="darcook/DCPV-Artifacts", repo_type="dataset")
+print(files)
+```
+
+## Citation
+
+If you use these artifacts, please cite our work (Anonymized for Blind Review).