pith:B7ZBUQTU
ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
ProDG generates high-fidelity visual prototypes for explaining neural network decisions using only the model's weights and no real data.
arxiv:2605.08858 v2 · 2026-05-09 · cs.CV
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\usepackage{pith}
\pithnumber{B7ZBUQTU2772JHIZAIU5YNYUIK}
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Record completeness
Claims
a novel framework that leverages generative models to synthesize pure, high-fidelity prototypes directly from the frozen model's weights, completely eliminating the dependency on any external data.
That generative models can reliably produce prototypes whose visual and semantic properties match those that would have been selected from real data, and that these synthetic prototypes yield faithful explanations for the original model's decisions.
ProDG uses generative models to create data-free prototypes from model weights for post-hoc explainability of image predictions.
Receipt and verification
| First computed | 2026-05-21T01:05:20.705874Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0ff21a4274d7ffa49d190229dc371442a7f8110f04f28f5d682b29d3a2a1e4ea
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/B7ZBUQTU2772JHIZAIU5YNYUIK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0ff21a4274d7ffa49d190229dc371442a7f8110f04f28f5d682b29d3a2a1e4ea
Canonical record JSON
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