lr_poly
lr_poly appears in the code Syntology harvested for 25 papers, as 6 distinct code bodies found in 25 places (a place is one code body under one paper). At least one of them ran in 25 of the papers; 6 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 lr_poly 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 6 of the 6 distinct code bodies named lr_poly; 0 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:
Licence is a property of each copy, so it is counted per place: 14 of the 25 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
25 papers shown of 25, newest first; 25 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; 1 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.
| Paper | Date | File | Status Syntology | Licence |
|---|---|---|---|---|
| Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds | 27 Mar 2024 | yuan-zm/DGT-ST/network/lr_adjust.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | Apache-2.0 (permissive) |
| Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels | 5 Mar 2024 | lizhuohong/segland/ft_pop.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection | 21 Dec 2023 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation | 29 Jul 2023 | XiaRho/CMDA/utils/utils.py a8ad52a812530368 |
ran fingerprinted | no licence file found · pointer only |
| Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions | 21 Jul 2022 | theo2021/OnDA/framework/domain_adaptation/methods/prototypes.py 9fec89dab050cb13 |
ran · honoured contract fingerprinted | GPL-2.0 (copyleft) · pointer only |
| Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank | 27 Apr 2021 | Shathe/SemiSeg-Contrastive/trainSSL.py 1b0ce6e9e98a57de |
ran · honoured contract fingerprinted | Apache-2.0 (permissive) |
| Improving One-stage Visual Grounding by Recursive Sub-query Construction | 3 Aug 2020 | zyang-ur/ReSC/model/loss.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| DACS: Domain Adaptation via Cross-domain Mixed Sampling | 17 Jul 2020 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning | 15 Jul 2020 | WilhelmT/ClassMix/trainSSL.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation | 13 Apr 2020 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation | 18 Mar 2020 | SHI-Labs/Unsupervised-Domain-Adaptation-with-Differential-Treatment/train_sim.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | no licence file found · pointer only |
| Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation | 2 Mar 2020 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic Segmentation | 12 Oct 2019 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| A Fast and Accurate One-Stage Approach to Visual Grounding | 18 Aug 2019 | zyang-ur/onestage_grounding/train_yolo.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| Semi-Supervised Semantic Segmentation with High- and Low-level Consistency | 15 Aug 2019 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision | 4 Jun 2019 | fregu856/evaluating_bdl/segmentation/ensembling_train_syn.py b91a29dacf5ca1a2 |
ran · honoured contract fingerprinted | MIT (permissive) |
| Domain Adaptation for Structured Output via Discriminative Patch Representations | 16 Jan 2019 | wasidennis/AdaptSegNet/pytorch_0.4/train_gta2cityscapes_multi.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | no licence file found · pointer only |
| UPSNet: A Unified Panoptic Segmentation Network | 12 Jan 2019 | uber-research/UPSNet/upsnet/upsnet_end2end_train.py 93a18ae852753bae |
ran · honoured contract fingerprinted | licence not identified · pointer only |
| Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation | 25 Sep 2018 | RoyalVane/CLAN/CLAN_train.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| Learning to Adapt Structured Output Space for Semantic Segmentation | 28 Feb 2018 | lym29/DASeg/train_gta2cityscapes_multi.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | no licence file found · pointer only |
| No More Discrimination: Cross City Adaptation of Road Scene Segmenters | 27 Apr 2017 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results | 6 Mar 2017 | identical code first harvested elsewhere b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | licence of this copy not recorded |
| Joint 2D-3D-Semantic Data for Indoor Scene Understanding | 3 Feb 2017 | jamycheung/trans4pass/adaptations/train_mpa.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | Apache-2.0 (permissive) |
| Learning Video Object Segmentation from Static Images | 8 Dec 2016 | omkar13/MaskTrack/training/utility_functions.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (permissive) |
| arXiv:136940019 | ETHRuiGong/TADA/trainTACS_coarsetofine_addcontrastive.py b3b7c1c716f4ca66 |
ran · honoured contract fingerprinted | MIT (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".
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