upsample
upsample: 11 implementations from 16 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 9 distinct outputs across 3 buckets, one shared input per bucket.
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:
- 1 more implementation of this name was examined but did not run on the shared input (RuntimeError 1).
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
Bucket 1 of 3: arg 1: rank 4, kind float, dtype float32
9 implementations from 14 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 7 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 5 papers 81878e8845f7 recorded values identical |
CBR/model.py a739072b recorded x:4/float/float32 models/flow_net.py 3d1a78e0 recorded x:4/float/float32; scale=2 |
[0.723105, 0.723105, 0.653079, 0.653079, 1.44609, 1.44609, 1.09854, 1.09854, …] shape [2, 3, 8, 8] · float32 · Tensor |
| 2 implementations 2 papers 227735dc8343 recorded values identical |
models/models.py a90ac42d recorded x:4/float/float32; scale_factor=2, mode='bilinear' basicsr/archs/FourierISP_arch.py b77fa862 recorded x:4/float/float32; fac=2 |
[0.723105, 0.705598, 0.670586, 0.851331, 1.24783, 1.3592, 1.18543, 1.09854, …] shape [2, 3, 8, 8] · float32 · Tensor |
| 1 implementation 3 papers ce8492085f92 |
lpips/lpips.py 8cafd18b recorded in_tens:4/float/float32; out_HW=(64, 64) |
[0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, …] shape [2, 3, 64, 64] · float32 · Tensor |
| 1 implementation 1 paper 0e1e08831bed |
models/uabcnet.py ddd3602a recorded x:4/float/float32; sf=4 |
[0.723105, 0, 0, 0, 0.653079, 0, 0, 0, …] shape [2, 3, 16, 16] · float32 · Tensor |
| 1 implementation 1 paper 2871c2d062cc |
model/moe.py da465d70 recorded x:4/float/float32; h=6, w=10 |
[0.723105, 0.657004, 0.587013, 0.653079, 0.914257, 1.23116, 1.44609, 1.42479, …] shape [2, 3, 6, 10] · float32 · Tensor |
| 1 implementation 1 paper 78b8dce6d505 |
perturb.py ab2289f7 recorded image:4/float/float32 |
[0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, 0.723105, …] shape [2, 3, 224, 224] · float32 · Tensor |
| 1 implementation 1 paper 9a6ea046abe8 |
utils.py 2ee51c95 recorded y:4/float/float32; scale_factor=4 |
[0.669999, 0.672634, 0.675725, 0.644, 0.624302, 0.675826, 0.849746, 1.1078, …] shape [2, 3, 16, 16] · float32 · Tensor |
Bucket 2 of 3: arg 1: rank 1, kind float, dtype float64
1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 1 paper f732d5f1db32 |
data/vctk/prep_vctk.py 7bb46d84 recorded x_lr:1/float/float64; r=3 |
[-0.903889, -0.785624, -0.377746, 0.157131, 0.656393, 0.957425, 0.897612, 0.399927, …] shape [24] · float64 · ndarray |
Bucket 3 of 3: arg 1: rank 1, kind int, dtype int64
1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 1 paper 41612408e879 |
research/Trainer.py a84f1865 recorded current_indices:1/int/int64; target_number=10 |
[2, 1, 3, 0, 1, 2, 2, 2, …] shape [10] · int64 · ndarray |
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/upsample.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