Paper Citation Record · LEDGER

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2504.15796.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.15796 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:22:25.092856Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact5
verified fuzzy21
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cfa4165-184d-4f8f-99c7-cc3a838d7154 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.909436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dada16d8-fe15-4a84-a592-0f04fecba38f · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.914486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 753da46f-cca1-4f1d-9b3f-a7bfae9c7012 · outbound

This paper cites Dynamic graph cnn for learning on point clouds,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Dynamic graph cnn for learning on point clouds,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.919446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c76de5b0-9751-4504-9d1d-473f95554202 · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Scannet: Richly-annotated 3d reconstructions of indoor scenes,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.924489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 623f0a72-4b42-4166-a5b9-7b5939800b21 · outbound

This paper cites Modelnet: Towards a datacenter emulation environment,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Modelnet: Towards a datacenter emulation environment,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.825361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a5a9a978-537c-4865-b132-e46e352666cd · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness ShapeNet: An Information-Rich 3D Model Repository

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.934418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eddd7fc0-242a-4b2f-87e4-043463216a82 · outbound

This paper cites Domain adaptation on point clouds via geometry-aware implicits,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Domain adaptation on point clouds via geometry-aware implicits,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.809184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c6c20d0-3c17-476d-9344-201f8abd3843 · outbound

This paper cites Joint supervised and self-supervised learning for 3d real world challenges,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Joint supervised and self-supervised learning for 3d real world challenges,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.790961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c50e725-fecd-49b2-9ea8-54e04344af70 · outbound

This paper cites Point cloud domain adaptation via masked local 3d structure predic- tion,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Point cloud domain adaptation via masked local 3d structure predic- tion,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.772242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a6917b1-cd22-41da-9aef-d55ea7409375 · outbound

This paper cites Geometry-aware self-training for unsupervised domain adaptation on object point clouds,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Geometry-aware self-training for unsupervised domain adaptation on object point clouds,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.755824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 743bfcfc-6161-4642-80f8-a7f4bc06dc91 · outbound

This paper cites Gra- dient surgery for multi-task learning,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Gra- dient surgery for multi-task learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.737452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e758db89-cfea-4dcb-ac22-0c07b500355b · outbound

This paper cites Metabalance: improving multi-task recommendations via adapting gradient magni- tudes of auxiliary tasks,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Metabalance: improving multi-task recommendations via adapting gradient magni- tudes of auxiliary tasks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.719826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 057a9735-e6ef-4f10-9e52-fb251a898ed5 · outbound

This paper cites Shortcut learning in deep neural networks,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Shortcut learning in deep neural networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.969514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba584c17-6283-4bad-a049-fe55fdbac0ca · outbound

This paper cites Self-distillation for unsupervised 3d domain adaptation,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Self-distillation for unsupervised 3d domain adaptation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.691236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b244c59d-1994-4184-8316-8f80d9328ea9 · outbound

This paper cites Self-supervised learning for domain adaptation on point clouds,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Self-supervised learning for domain adaptation on point clouds,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.675256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2da3a179-7759-4128-a286-717a80f9e54b · outbound

This paper cites Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:22:25.388580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0d31b87-0cf9-411c-ac5a-89651c9af887 · outbound

This paper cites Self-supervised global-local structure modeling for point cloud domain adaptation with reliable voted pseudo labels,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Self-supervised global-local structure modeling for point cloud domain adaptation with reliable voted pseudo labels,

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-16T11:22:25.364780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8111add4-2947-46b2-9917-7acb91790f68 · outbound

This paper cites Point cloud domain adaptation via masked local 3d structure prediction,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Point cloud domain adaptation via masked local 3d structure prediction,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:24.994000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb4f0bf-1770-4492-a2ed-de423dac9fad · outbound

This paper cites A Survey of Dataset Refinement for Problems in Computer Vision Datasets.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness A Survey of Dataset Refinement for Problems in Computer Vision Datasets

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:22:25.298499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3cac7884-c4ca-4ce1-b8df-a7642b4b1989 · outbound

This paper cites A review of instance selection methods,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness A review of instance selection methods,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.658639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a911687-4a08-4685-b898-0c3881ab83b4 · outbound

This paper cites C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.642892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9b935efb-a805-4d27-ad4d-0b8c7365884e · outbound

This paper cites Crssc: salvage reusable samples from noisy data for robust learning,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Crssc: salvage reusable samples from noisy data for robust learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.626168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f0c4c30-41a3-4bc4-8ce4-afb457495a1c · outbound

This paper cites Distribution alignment: A unified framework for long-tail visual recognition,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Distribution alignment: A unified framework for long-tail visual recognition,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.017993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e39786b-dd5f-4c07-be53-2f03549539c4 · outbound

This paper cites Classification with noisy labels by importance reweighting,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Classification with noisy labels by importance reweighting,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.600095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ff156a72-45d3-4afb-9807-0ac2c0141405 · outbound

This paper cites Dualgraph: A graph-based method for reasoning about label noise,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Dualgraph: A graph-based method for reasoning about label noise,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.583813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 341b768b-501d-4037-a2e8-5e7dd78d46d2 · outbound

This paper cites Conflict-averse gradi- ent descent for multi-task learning,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Conflict-averse gradi- ent descent for multi-task learning,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.033304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d73d08a0-2684-48d5-b13a-e5a427f1787f · outbound

This paper cites A model- agnostic approach to mitigate gradient interference for multi-task learn- ing,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness A model- agnostic approach to mitigate gradient interference for multi-task learn- ing,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.557706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cefea64f-b637-40c8-a2ef-7dc61aa97d1a · outbound

This paper cites Self-supervised deep learning on point clouds by reconstructing space,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Self-supervised deep learning on point clouds by reconstructing space,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.541802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d1b594d-9a12-46d9-8815-95630432982d · outbound

This paper cites Pointcloud saliency maps,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Pointcloud saliency maps,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.524531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bef4393b-735c-45b4-b749-26ef30a5e2a8 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness 3d shapenets: A deep representation for volumetric shapes,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.051544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cb5c4a6-bb03-4ef7-9c53-877474adf5ef · outbound

This paper cites Convolutional neural networks on surfaces via seamless toric covers.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Convolutional neural networks on surfaces via seamless toric covers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.056083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88b72890-547a-488b-888e-bd9cf7484f94 · outbound

This paper cites Self-distillation for unsupervised 3d domain adaptation,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Self-distillation for unsupervised 3d domain adaptation,

Reference 32

Resolution: verified exact; raw_fallback, observed 2026-08-16T11:22:25.273868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a272af0-1c3b-4d10-af3c-671e5f0b39da · outbound

This paper cites Pointdan: A multi-scale 3d domain adaption network for point cloud representation,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Pointdan: A multi-scale 3d domain adaption network for point cloud representation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.488622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ac91ecf6-4c7c-4f3f-892f-27fe6540e118 · outbound

This paper cites Dfan: Dual-branch feature alignment network for domain adaptation on point clouds,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Dfan: Dual-branch feature alignment network for domain adaptation on point clouds,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.473342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a90f0d11-d862-4764-9f33-42f1fa7c3aa3 · outbound

This paper cites Nonlinear causal discovery with additive noise models,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Nonlinear causal discovery with additive noise models,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.074576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7548c6b-f468-4f2b-adae-5015896923e0 · outbound

This paper cites Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:22:25.148024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e25808a-3f9d-4ac0-a193-f5af15deaa00 · outbound

This paper cites Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:22:25.083959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c7179f2-ad41-4ca9-b213-42a4f7d13ca3 · outbound

This paper cites Loss-balanced task weighting to reduce negative transfer in multi-task learning,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Loss-balanced task weighting to reduce negative transfer in multi-task learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.437010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d6ee3e32-27b4-4881-ae51-17125299c6c6 · outbound

This paper cites Dynamic task prioritization for multitask learning,.

Locating and Mitigating Gradient Conflicts in Point Cloud Domain Adaptation via Saliency Map Skewness Dynamic task prioritization for multitask learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:22:25.421105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.