Pith Number
pith:ITJL6SXG
pith:2024:ITJL6SXGHZGSD7GX7QCTGJHSUJ
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Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?
arxiv:2409.10775 v1 · 2024-09-16 · cs.CV · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{ITJL6SXGHZGSD7GX7QCTGJHSUJ}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Cited by
Receipt and verification
| First computed | 2026-07-05T09:07:56.780729Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
44d2bf4ae63e4d21fcd7fc053324f2a278a69917807af19c3e48e8721416511f
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ITJL6SXGHZGSD7GX7QCTGJHSUJ \
| 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: 44d2bf4ae63e4d21fcd7fc053324f2a278a69917807af19c3e48e8721416511f
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "ab4d486a1f19536411de3b9420ea0fadc0247ff3796fc5d9d5002651a73b00ea",
"cross_cats_sorted": [
"cs.AI",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by-sa/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2024-09-16T23:21:22Z",
"title_canon_sha256": "d8c9fc184a6c201007de82cf5fbf67e1271c1f70c4a22f7585428bce906fe870"
},
"schema_version": "1.0",
"source": {
"id": "2409.10775",
"kind": "arxiv",
"version": 1
}
}