pith:XPDYHPWS
Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
Aggregating standardized cross-validation losses produces reliable sample-level scores for detecting noisy labels.
arxiv:2605.10165 v2 · 2026-05-11 · cs.CV · cs.AI
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\pithnumber{XPDYHPWSDUVU4UVQ7KMDA7EGAT}
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Record completeness
Claims
SLA generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores.
That deviations in fold-level validation losses across cross-validation runs are caused primarily by label noise rather than by model randomness, data split effects, or other unmodeled factors.
SLA converts hard-counting of high-loss samples into a continuous noisiness score by standardizing fold-level validation losses and aggregating them over multiple cross-validation runs, showing better performance than baselines on fundus data.
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Receipt and verification
| First computed | 2026-05-21T01:05:20.893991Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
bbc783bed21d2b4e52b0fa98307c8604e1182b29d14ff06ffad526053fc12333
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XPDYHPWSDUVU4UVQ7KMDA7EGAT \
| 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: bbc783bed21d2b4e52b0fa98307c8604e1182b29d14ff06ffad526053fc12333
Canonical record JSON
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