Pith Number
pith:FYE57ADB
pith:2018:FYE57ADBDWK7MFT2UZRMJNHKWL
not attested
not anchored
not stored
refs pending
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
arxiv:1801.06397 v3 · 2018-01-19 · cs.CV · stat.ML
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{FYE57ADBDWK7MFT2UZRMJNHKWL}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
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.
Receipt and verification
| First computed | 2026-05-18T00:20:23.939229Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FYE57ADBDWK7MFT2UZRMJNHKWL \
| 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: 2e09df80611d95f6167aa662c4b4eab2ffef124140e362b520eb390f49931717
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "ec3b843fc7a1ee1f2b61694ea07067d25a0cf32d607a03ca3879985321f616ad",
"cross_cats_sorted": [
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.CV",
"submitted_at": "2018-01-19T13:21:07Z",
"title_canon_sha256": "4165d13d76c93b252891c01b32e6f1393060c8157eccc7b197cc189c350e49ca"
},
"schema_version": "1.0",
"source": {
"id": "1801.06397",
"kind": "arxiv",
"version": 3
}
}