pith:I2BCLRTF
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Benchmarks for common corruptions show negligible relative robustness gains from AlexNet to ResNet.
arxiv:1903.12261 v1 · 2019-03-28 · cs.LG · cs.CV · stat.ML
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Claims
We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers.
That the 15 chosen corruptions and the perturbations in ImageNet-P sufficiently represent the common real-world image degradations classifiers will encounter in deployment.
Introduces ImageNet-C and ImageNet-P benchmarks revealing negligible robustness gains from AlexNet to ResNet models on common corruptions and perturbations, plus methods to improve them.
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| First computed | 2026-07-04T23:30:50.939148Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
468225c665c67c794f2afc09d157789b8056d8fa255c002ecfa5ea2f5ec4fd95
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/I2BCLRTFYZ6HSTZK7QE5CV3YTO \
| 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: 468225c665c67c794f2afc09d157789b8056d8fa255c002ecfa5ea2f5ec4fd95
Canonical record JSON
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