Initial release: VITS-AD evaluation suite (regime labels, ledgers, multiseed scores, sample renderings)

README.md

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+---
+license: mit
+language:
+- en
+pretty_name: VITS-AD Evaluation Suite
+size_categories:
+- 10K<n<100K
+task_categories:
+- time-series-forecasting
+tags:
+- anomaly-detection
+- time-series
+- evaluation
+- benchmark
+- frozen-vision
+- regime-analysis
+- negative-results
+- neurips-2026
+---
+
+# VITS-AD Evaluation Suite
+
+**Companion artifact for the NeurIPS 2026 Evaluations & Datasets (E&D) Track
+submission *VITS-AD: A Regime-Aware Evaluation Suite for Frozen-Vision
+Time-Series Anomaly Detection*.**
+
+This dataset is **not a new corpus**. It bundles the *evaluation outputs*
+produced by the VITS-AD pipeline and the raw-space Mahalanobis baseline so
+that future work can:
+
+1. **Reproduce paper tables and statistical tests** without re-running the
+   full vision pipeline.
+2. **Run paired comparisons** (Wilcoxon, paired-99 UCR) directly on the
+   per-window scores.
+3. **Audit the regime classification** (amplitude vs. structural) against the
+   underlying evidence artifacts.
+
+The submission is **double-blind**; this dataset card is anonymous. Source
+code is at `https://github.com/evaldataset/VITS-AD` (reviewer-routed via
+`anonymous.4open.science`).
+
+## Contents
+
+| Folder              | Purpose                                                   | Size  |
+|---------------------|-----------------------------------------------------------|-------|
+| `regime_labels/`    | Per-dataset regime annotation + classifier features       | <1 KB |
+| `ledgers/`          | JSON ledgers underlying main-paper claims                 | 64 KB |
+| `ucr_canonical/`    | UCR 109/99 aggregated and per-series metrics              | 168 KB |
+| `multiseed_scores/` | Per-window scores + labels for 5 seeds × {LP,RP} × {PSM,MSL,SMAP}, no model weights | 9.8 MB |
+| `sample_renderings/`| Pipeline diagram and regime-gain figures                  | 1 MB  |
+
+Total: ~11 MB.
+
+## What this is for (E&D Track scope)
+
+The submission's contribution is **benchmark analysis and evaluation
+methodology**, not a new dataset. We therefore distribute:
+
+- The **regime axis** (amplitude vs. structural) along which vision
+  rendering does and does not pay off.
+- The **paired-99 UCR comparison** that demonstrates Wilcoxon $p<10^{-7}$
+  in favour of the vision pipeline on the structural univariate regime.
+- The **per-seed scores** that allow re-running paired Wilcoxon and
+  bootstrap confidence intervals on the multiseed PSM/MSL/SMAP runs.
+- The **calibration and FPS ledgers** that back the compute-disclosure and
+  CalibGuard tables in the paper supplement.
+
+We do **not** redistribute the raw benchmark datasets (SMD, PSM, MSL, SMAP,
+UCR Anomaly Archive). License and download paths for those upstream
+benchmarks are listed in the paper's Asset Credits table.
+
+## Files
+
+### `regime_labels/regime_labels.json`
+
+Per-dataset regime label, channel count, raw vs. VITS-AD AUC-ROC, and the
+five classifier features. The accompanying notes record the in-sample
+classifier accuracy ($90.1\%$), the majority-class baseline ($88.1\%$), and
+the leave-one-dataset-out CV collapse to chance — i.e., the regime axis is
+**descriptive**, not a deployable predictor.
+
+### `ledgers/`
+
+| File | Backed claim |
+|------|--------------|
+| `improved_ensemble_results.json` | SMD 28-entity macro AUC-ROC for VITS-AD vs. raw Mahalanobis |
+| `multiseed_results.json` | $n=5$ seed mean ± std for PSM/MSL/SMAP × {LP, RP} |
+| `multiseed_ensemble_summary.json` | Rank-mean ensemble across renderers per dataset |
+| `optimized_ensemble.json` | Oracle renderer-adaptive scoring |
+| `calibguard_multidataset.json` | Realized FAR vs. target FAR (empirical diagnostic) |
+| `fps_benchmark.json` | FPS, parameter count, and compute disclosure |
+| `clip_backbone_comparison.json` | DINOv2 vs. CLIP backbone ablation |
+| `ucr_results.json` | Legacy UCR aggregate (paired-99 in `ucr_canonical/`) |
+| `view_disagree_sweep.json` | Cross-view disagreement scoring sweep |
+
+### `ucr_canonical/`
+
+Authoritative UCR ledgers used by every UCR claim in the paper:
+
+| File | Description |
+|------|-------------|
+| `summary.json`     | 109-series VITS-AD aggregate |
+| `paired_99.json`   | 99-series paired comparison vs. raw Mahalanobis |
+| `combined_109.json`| Per-series VITS-AD scores |
+| `per_series.json`  | Per-series metric breakdown |
+| `eligible_list.json` | List of the 109 eligible UCR series |
+| `ucr_canonical.json` | Combined manifest |
+
+### `multiseed_scores/`
+
+Layout: `multiseed_scores/{psm,msl,smap}/{line_plot,recurrence_plot}/seed_{42,123,456,789,2024}/`
+
+Each leaf directory contains:
+
+- `scores.npy` — per-window anomaly score (float64)
+- `labels.npy` — per-window ground-truth label (int64, $\{0,1\}$)
+- `metrics.json` — AUC-ROC, AUC-PR, best-F1, F1-PA for that seed
+
+Model checkpoints (`best_model.pt`) are intentionally **not** redistributed
+to keep the bundle compact; they can be regenerated from the source repo.
+
+### `sample_renderings/`
+
+PDFs of the pipeline diagram (Figure 1), per-dataset regime gain
+(Figure 3a), and the regime-map scatter (Figure 4 in the supplement).
+
+## Reproducing the paper's statistical tests
+
+```python
+import json, numpy as np
+from scipy.stats import wilcoxon
+
+base = "multiseed_scores/psm/line_plot"
+seeds = [42, 123, 456, 789, 2024]
+aucs = []
+for s in seeds:
+    m = json.load(open(f"{base}/seed_{s}/metrics.json"))
+    aucs.append(m["auc_roc"])
+print(f"PSM-LP mean ± std: {np.mean(aucs):.4f} ± {np.std(aucs, ddof=1):.4f}")
+
+# Paired-99 UCR Wilcoxon (vision vs. raw Mahalanobis)
+paired = json.load(open("ucr_canonical/paired_99.json"))
+stat, p = wilcoxon(paired["vits_ad_auc"], paired["raw_maha_auc"])
+print(f"Paired-99 UCR Wilcoxon p={p:.2e}")
+```
+
+## License
+
+MIT. All redistributed JSON ledgers, regime annotations, and rendered
+example PDFs are original work of the (anonymous) authors and are released
+under MIT alongside the source repository.
+
+## Citation
+
+```bibtex
+@inproceedings{vitsad2026,
+  title  = {{VITS-AD}: A Regime-Aware Evaluation Suite for Frozen-Vision Time-Series Anomaly Detection},
+  author = {Anonymous},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},
+  year   = {2026}
+}
+```

ledgers/calibguard_multidataset.json

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+{
+  "alpha_values": [
+    0.01,
+    0.05,
+    0.1
+  ],
+  "results": [
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+      }
+    },
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+    }
+  ]
+}

ledgers/clip_backbone_comparison.json

@@ -0,0 +1,18 @@
+{
+  "clip_line_plot": {
+    "auc_pr": 0.6036196001345208,
+    "auc_roc": 0.8707853035505371
+  },
+  "clip_recurrence_plot": {
+    "auc_pr": 0.464941742047615,
+    "auc_roc": 0.7713926702538144
+  },
+  "dinov2_line_plot": {
+    "auc_pr": 0.3160005703709965,
+    "auc_roc": 0.7071254427632818
+  },
+  "dinov2_recurrence_plot": {
+    "auc_pr": 0.5727017633385711,
+    "auc_roc": 0.870398408997204
+  }
+}

ledgers/fps_benchmark.json

@@ -0,0 +1,25 @@
+{
+  "rendering_fps": {
+    "line_plot": 12.974609062025229,
+    "recurrence_plot": 313.29909591710623
+  },
+  "backbone_fps": {
+    "dinov2": 228.9713787009395,
+    "clip": 295.30419259047704
+  },
+  "predictor_fps": {
+    "default_2M": 904.1485049786883,
+    "improved_6M": 264.4656994678912
+  },
+  "e2e_fps": {
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+    "dinov2_rp": 21.69710898324057
+  },
+  "params": {
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+    "improved": 5927424
+  },
+  "hardware": "RTX 3090",
+  "batch_size": 64,
+  "notes": "Single GPU, batch_size=64, averaged over 100 batches"
+}

ledgers/improved_ensemble_results.json

@@ -0,0 +1,158 @@
+{
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+      "smooth_method": "median",
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+      "w_lp": 0.4
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+    "individual_rp_avg": 0.7751144086141056,
+    "individual_rp_count": 28,
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+        "f1_pa": 0.8045454545454547
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+      "machine-3-7": {
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+        "f1_pa": 0.6074074074074074
+      },
+      "machine-3-8": {
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+      "machine-3-9": {
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+        "auc_roc": 0.9232472940085152,
+        "f1_pa": 0.7887323943661971
+      }
+    }
+  }
+}

ledgers/multiseed_ensemble_summary.json

@@ -0,0 +1,38 @@
+{
+  "psm": {
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+      0.5947063089103468,
+      0.6031968558018207,
+      0.5937262101737255,
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+    "std": 0.006372326220930482,
+    "std_type": "sample"
+  },
+  "msl": {
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+      0.5638884816539723,
+      0.5577131192906611,
+      0.5648472357651508,
+      0.5616058115642594
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+    "mean": 0.5616049165945008,
+    "std": 0.002895969433571757,
+    "std_type": "sample"
+  },
+  "smap": {
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+      0.6782235110939374,
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ledgers/optimized_ensemble.json

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ledgers/ucr_results.json

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ledgers/view_disagree_sweep.json

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