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conv2d_transpose

Syntologyentry name in harvested coderead from the graph 2026-09-24

conv2d_transpose appears in the code Syntology harvested for 18 papers, as 12 distinct code bodies found in 18 places (a place is one code body under one paper). At least one of them ran in 1 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named conv2d_transpose do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 1 of the 12 distinct code bodies named conv2d_transpose; 11 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
11unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 18 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

18 papers shown of 18, newest first; 18 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 2 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers 3 Jan 2024 easyry/cra-pcn/pointnet_utils/tf_util.py 8632d18c12be28a9 unverified no licence file found · pointer only
PHG-Net: Persistent Homology Guided Medical Image Classification 28 Nov 2023 yaoppeng/topoclassification/models/pointnet/layers_tf.py 9ba01df4045b7ebb unverified no licence file found · pointer only
GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds 25 Aug 2023 chenchao15/GridPull/tf_util.py eb8cf1976d6c83e3 ran no licence file found · pointer only
Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors 25 Mar 2023 chenchao15/NeuralTPS/tf_util.py 8632d18c12be28a9 unverified MIT (permissive)
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning 21 May 2021 azuki-miho/RFCR/RandLA_Net_S3DIS/helper_tf_util.py 02f9433ac33f32b3 unverified MIT (permissive)
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation 6 Apr 2021 PointCloudYC/SQN_tensorflow/helper_tf_util.py 02f9433ac33f32b3 unverified MIT (permissive)
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges 7 Sep 2020 QingyongHu/SensatUrban/tf_util.py 02f9433ac33f32b3 unverified MIT (permissive)
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation 17 Aug 2020 FilippoAleotti/Reversing/mono/ops.py bf9685b92676e2dc unverified Apache-2.0 (permissive)
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud 26 Jun 2020 Salingo/RPM-Net/code/utils/tf_util.py 5d5a54e9e61f622c unverified MIT (permissive)
Path-Invariant Map Networks 31 Dec 2018 zaiweizhang/path_invariance_map_network/tf_util.py 5d5a54e9e61f622c unverified BSD-3-Clause (permissive)
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud 8 Dec 2018 ericyi/GSPN/utils/tf_util.py 901a0f27987b65ef unverified MIT (permissive)
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes 30 Nov 2018 hjwdzh/TextureNet/src/models/tf_util.py 5d5a54e9e61f622c unverified MIT (permissive)
Generating 3D Adversarial Point Clouds 19 Sep 2018 LONG-9621/Generating-3D-Adversarial-Point-Clouds/utils/tf_util.py 8632d18c12be28a9 unverified MIT (permissive)
Local Spectral Graph Convolution for Point Set Feature Learning 15 Mar 2018 fate3439/LocalSpecGCN/utils/tf_util.py 2334d046d6229ebc unverified MIT (permissive)
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks 19 Nov 2015 MustafaMustafa/dcgan-tf-benchmark/dcgan/ops.py a78243a3c08ae288 unverified MIT (permissive)
U-Net: Convolutional Networks for Biomedical Image Segmentation 18 May 2015 ImagingLab/Colorizing-with-GANs/src/networks.py e5e530369c9ef0a1 unverified Apache-2.0 (permissive)
arXiv:Wang_PWCLO-Net_Deep_LiDAR_Odometry_in_3D_Point_Clouds_Using_Hierarchical_CVPR_2021_paper IRMVLab/PWCLONet/utils/tf_util.py 95206c1983206ca2 unverified MIT (permissive)
arXiv:136620053 Tianxinhuang/PCDNet/tf_util.py 6a0c1bc33fced52e unverified Apache-2.0 (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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