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DoubleConv

Syntologyfunction-name censuscensus 2026-09-22battery b986f7e04d79all samples with this name

DoubleConv: 8 implementations from 7 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 6 distinct outputs.

Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.

Not compared, and not in the tables or the counts above:

Bucket 1 of 1: arg 1: rank 4, kind float, dtype float32

8 implementations from 7 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 6 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.

Cluster (same digest to six decimals)MembersShared output on this bucket's input
2 implementations
2 papers
195a614a1570
recorded values identical
class
model.py 3bc37018
recorded x:4/float/float32
class
demo/UNet.py f416b4cf
recorded x:4/float/float32
[0.148105, 0, 0.686573, 0.709454, 1.14974, 0.0821147, 0, 1.05254, …]
shape [2, 4, 4, 4] · float32 · Tensor
2 implementations
2 papers
597e83da3592
recorded values identical
class
networks/UNet.py 2d7adb8e
recorded x:4/float/float32
class
src/model.py c849a67d
recorded x:4/float/float32
[0, 0, 0, 0, 0, 0, 0.921028, 1.05611, …]
shape [2, 4, 4, 4] · float32 · Tensor
1 implementation
1 paper
427c4ec189d4
class
model/unet.py 6224b178
recorded x:4/float/float32
[0.294728, 0.312427, 0, 0.0397796, 0, 0.0567878, 0.0922604, 1.17113, …]
shape [2, 4, 4, 4] · float32 · Tensor
1 implementation
1 paper
59450a11473a
class
src/models/fluxnet_d_2d.py d7ae316f
recorded x:4/float/float32
[0.265055, 0, 0, 0, 0.411668, 0, 0.247691, 1.85653, …]
shape [2, 4, 4, 4] · float32 · Tensor
1 implementation
1 paper
5b07d37cfc90
class
models/unet3dhybrid_dynamicfusion.py 22f2b190
recorded x:4/float/float32
[-0.543543, -0.543543, -0.543543, -0.543543, -0.543543, -0.543543, 1.34425, 1.24729, …]
shape [2, 4, 4, 4] · float32 · Tensor
1 implementation
1 paper
cbcad7691605
class
models/cover.py adc9b6ed
recorded x:4/float/float32
[1.0872, -0.214958, -0.241613, 0.0200114, 0.516234, 1.54185, 0.514694, 0.683796, …]
shape [2, 16, 4, 4] · float32 · Tensor

Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/doubleconv.json.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections