Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data | Paper Citation Record

Observation cb0e61f5-13a1-45c0-b3d8-06cd7f61421c · outbound

This paper cites Metareg: Towards domain generalization using meta- regularization.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Metareg: Towards domain generalization using meta- regularization

Reference 1

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Observation 1c37c383-d639-410c-95ad-0051b3bbee9a · outbound

This paper cites Using sim- ulation and domain adaptation to improve efﬁciency of deep robotic grasping.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Using sim- ulation and domain adaptation to improve efﬁciency of deep robotic grasping

Reference 2

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Observation 052e514b-1bcb-4f43-a732-6618a716fbb4 · outbound

This paper cites Road: Reality ori- ented adaptation for semantic segmentation of urban scenes.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Road: Reality ori- ented adaptation for semantic segmentation of urban scenes

Reference 3

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Observation fd2e996d-7fd0-4779-a59c-094023f1cb91 · outbound

This paper cites Multi- column deep neural networks for image classiﬁcation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Multi- column deep neural networks for image classiﬁcation

Reference 4

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Observation b29cf752-6d7c-4138-b1cc-d1aa62a4e64d · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data The cityscapes dataset for semantic urban scene understanding

Reference 5

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Observation 3ba1cb17-6697-4a59-9b5a-405c3e91a984 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data AutoAugment: Learning Augmentation Policies from Data

Reference 6

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Observation a7369a6b-04fe-4684-a8c0-c70947e09f8d · outbound

This paper cites Embodied question answer- ing.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Embodied question answer- ing

Reference 7

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Observation 9c00773b-6126-4fda-b1d7-40868547a981 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Imagenet: A large-scale hierarchical image database

Reference 8

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Observation 8b4c3e4a-eec5-481b-80b2-7e1ff6695132 · outbound

This paper cites Dataset Augmentation in Feature Space.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Dataset Augmentation in Feature Space

Reference 9

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Observation d0d15906-590b-4a0f-b029-d5812f98f1b3 · outbound

This paper cites CARLA: An open urban driving simulator.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data CARLA: An open urban driving simulator

Reference 10

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Observation 5975a7a6-ff0b-4406-9f71-2f70b178f1a0 · outbound

This paper cites Counterexample-Guided Data Augmentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Counterexample-Guided Data Augmentation

Reference 11

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Observation 3fd7a1cb-a683-434f-a6ed-6aa19a4b1ef7 · outbound

This paper cites Learning at- tributes equals multi-source domain generalization.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Learning at- tributes equals multi-source domain generalization

Reference 12

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Observation 5963958a-372f-4418-99bc-33d51f984be4 · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Unsupervised domain adaptation by backpropagation

Reference 13

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Observation 9a08f248-6428-46c0-8c8b-e846a19d6557 · outbound

This paper cites Domain generalization for object recog- nition with multi-task autoencoders.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Domain generalization for object recog- nition with multi-task autoencoders

Reference 14

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Observation 3861bce5-8222-40d1-9738-9fc805f37cb5 · outbound

This paper cites Reshaping visual datasets for domain adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Reshaping visual datasets for domain adaptation

Reference 15

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Observation f29bfdd5-9e54-43d9-9981-c1fbdcf603cb · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Spatial pyramid pooling in deep convolutional networks for visual recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.499215Z

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Observation 94442fd6-8e5e-4089-84b4-6be94d434dba · outbound

This paper cites Deep residual learning for image recognition.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Deep residual learning for image recognition

Reference 17

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Observation 0291ad9c-349c-49c9-875a-8389a0ec9242 · outbound

This paper cites Efros, and Trevor Dar- rell.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Efros, and Trevor Dar- rell

Reference 18

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Observation 05f73919-9183-48ef-bf02-483eb7cf6b54 · outbound

This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 19

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Observation 04fc8050-f3a6-43b1-bb17-d497e2145492 · outbound

This paper cites Conditional generative adversarial network for struc- tured domain adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Conditional generative adversarial network for struc- tured domain adaptation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.470414Z

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Observation b86661bf-2ea3-412d-811d-e0e79c239e49 · outbound

This paper cites Domain transfer through deep activation matching.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Domain transfer through deep activation matching

Reference 21

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Observation a2795c66-1817-4c52-bd9c-785a9aa5f5f6 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Adam: A Method for Stochastic Optimization

Reference 22

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Observation 6eac0cdc-1adf-41fc-a40b-719ebe0bb742 · outbound

This paper cites AI2-THOR: An Interactive 3D Environment for Visual AI.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data AI2-THOR: An Interactive 3D Environment for Visual AI

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T05:39:19.817301Z

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Observation 5d6913b3-83f3-4283-8ee9-c1ab06ada7e3 · outbound

This paper cites Be- yond bags of features: spatial pyramid matching for rec- ognizing natural scene categories.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Be- yond bags of features: spatial pyramid matching for rec- ognizing natural scene categories

Reference 24

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Observation 3a82ea13-d430-4699-9189-75e5ec376983 · outbound

This paper cites Smart augmentation learning an optimal data augmentation strategy.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Smart augmentation learning an optimal data augmentation strategy

Reference 25

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Observation 737f6789-bb46-4f9f-a515-8c18ae5ff86b · outbound

This paper cites Deeper, broader and artier domain generaliza- tion.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Deeper, broader and artier domain generaliza- tion

Reference 26

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Observation a2caf1fb-74e8-4cd0-a9ef-7c322137de0d · outbound

This paper cites Learning to generalize: Meta-learning for do- main generalization.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Learning to generalize: Meta-learning for do- main generalization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.417142Z

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Observation d569d5f4-f2fb-4d15-ae41-f06b67d2bc02 · outbound

This paper cites Domain generalization with adversarial feature learning.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Domain generalization with adversarial feature learning

Reference 28

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Observation 4f09e075-b57c-4568-8ee0-1983dfcee488 · outbound

This paper cites Laplacian-steered neural style transfer.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Laplacian-steered neural style transfer

Reference 29

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Observation 2ca95dff-6c5b-48df-8674-edb244bcc644 · outbound

This paper cites Bidirectional Learning for Domain Adaptation of Semantic Segmentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Bidirectional Learning for Domain Adaptation of Semantic Segmentation

Reference 30

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Observation f912edb3-fb4a-4bd7-ab77-2873c7bf2f69 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Fully convolutional networks for semantic segmentation

Reference 31

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Observation 4e9ed1fc-2e63-43af-b939-fcc45e801ba9 · outbound

This paper cites Taking a closer look at domain shift: Category- level adversaries for semantics consistent domain adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Taking a closer look at domain shift: Category- level adversaries for semantics consistent domain adaptation

Reference 32

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Observation 81a2ed00-6a58-4951-8c70-882964df319c · outbound

This paper cites Domain generalization via invariant fea- ture representation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Domain generalization via invariant fea- ture representation

Reference 33

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Observation 9fcd1df3-6a4e-472c-9864-320f548737f6 · outbound

This paper cites Image to Image Translation for Domain Adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Image to Image Translation for Domain Adaptation

Reference 34

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Observation 45e8635d-5d3a-4ff3-afeb-c27d81803418 · outbound

This paper cites The mapillary vistas dataset for semantic understanding of street scenes.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data The mapillary vistas dataset for semantic understanding of street scenes

Reference 35

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Observation 65e4a2ea-a744-4c93-a2ed-f583d57912d7 · outbound

This paper cites Two at once: Enhancing learning and generalization capacities via ibn-net.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Two at once: Enhancing learning and generalization capacities via ibn-net

Reference 36

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Observation 7c0efa80-a418-4da8-b80f-d329ad90255d · outbound

This paper cites VisDA: The Visual Domain Adaptation Challenge.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data VisDA: The Visual Domain Adaptation Challenge

Reference 37

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Observation efc88897-42bd-4a74-88d8-1d8e70fd815d · outbound

This paper cites Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data

Reference 38

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Observation e3f7b1a8-3ea1-4883-af67-fc42b9fabd6e · outbound

This paper cites Playing for data: Ground truth from computer games.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Playing for data: Ground truth from computer games

Reference 39

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Observation 75d02236-2bc1-4407-ada4-f396a6c3a50a · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.336927Z

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Observation 68d59ab9-72e2-490a-8142-4cd475072914 · outbound

This paper cites CAD2RL: Real Single-Image Flight without a Single Real Image.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data CAD2RL: Real Single-Image Flight without a Single Real Image

Reference 41

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Observation cefb852c-1a9f-4e84-834f-db0a61f9e15a · outbound

This paper cites Maximum Classifier Discrepancy for Unsupervised Domain Adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Reference 42

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Observation 564b8934-b5c4-4dd2-84e5-c672dbea1953 · outbound

This paper cites Learning from synthetic data: Addressing domain shift for semantic segmentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Learning from synthetic data: Addressing domain shift for semantic segmentation

Reference 43

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Observation 10f310a1-0ec3-4902-a866-03198d529a54 · outbound

This paper cites APAC: Augmented PAttern Classification with Neural Networks.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data APAC: Augmented PAttern Classification with Neural Networks

Reference 44

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Observation 1d458e2a-146d-4ded-9002-9870ba57d8ad · outbound

This paper cites Best practices for convolutional neural networks applied to visual document analysis.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Best practices for convolutional neural networks applied to visual document analysis

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.318388Z

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Observation 8dcd967c-03c3-47b7-b79f-31b6204dd4fa · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 46

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Observation ba837339-161d-4aac-8836-129c9df3ad1a · outbound

This paper cites Implicit 3d orientation learning for 6d object detection from rgb images.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Implicit 3d orientation learning for 6d object detection from rgb images

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.309008Z

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Observation 66f64b4a-72b1-441b-a175-cbb75497d071 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Domain randomization for transferring deep neural networks from simulation to the real world

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.301276Z

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Observation 623cbab0-344b-4f1b-ad79-00bc58646547 · outbound

This paper cites A bayesian data augmentation approach for learn- ing deep models.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data A bayesian data augmentation approach for learn- ing deep models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.291852Z

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Observation 39d2ade1-f22e-4a89-a954-bc80ac6cb339 · outbound

This paper cites Training deep networks with synthetic data: Bridging the reality gap by domain randomization.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Training deep networks with synthetic data: Bridging the reality gap by domain randomization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.281692Z

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Observation 58ea7614-d711-40b7-bc10-c261d703929b · outbound

This paper cites Learning to adapt structured output space for semantic seg- mentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Learning to adapt structured output space for semantic seg- mentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.272516Z

Source-reported events for the cited work

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Observation a6013c0b-c388-4d04-8bde-7ca155bf8f93 · outbound

This paper cites Regularization of neural networks using drop- connect.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Regularization of neural networks using drop- connect

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.262783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5e1d4c3-a428-4b4d-babe-97cfbe79019f · outbound

This paper cites DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:39:19.996990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffdedee3-0110-4579-8865-9fe064b23de3 · outbound

This paper cites Zamir, Zhi-Yang He, Alexander Sax, Jiten- dra Malik, and Silvio Savarese.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Zamir, Zhi-Yang He, Alexander Sax, Jiten- dra Malik, and Silvio Savarese

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.253502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d518100-fd01-4357-84bb-6f7899721f35 · outbound

This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-14T05:39:19.924300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e8c0b1-38cd-4132-a07d-a56d7bcb2499 · outbound

This paper cites A lidar point cloud generator: from a virtual world to autonomous driving.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data A lidar point cloud generator: from a virtual world to autonomous driving

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.244020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 44c1b62c-cff4-4591-9952-d794bf947fd9 · outbound

This paper cites A curriculum domain adaptation approach to the se- mantic segmentation of urban scenes.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data A curriculum domain adaptation approach to the se- mantic segmentation of urban scenes

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.234502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eaa9e059-9820-4377-8c99-b562cc8d9355 · outbound

This paper cites Curricu- lum domain adaptation for semantic segmentation of urban scenes.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Curricu- lum domain adaptation for semantic segmentation of urban scenes

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.224157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a234ca5-cf25-4c8e-a7d5-7b92883ab2a1 · outbound

This paper cites Fully convolutional adaptation networks for seman- tic segmentation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Fully convolutional adaptation networks for seman- tic segmentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.212869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8bd3a248-21cb-49d6-9cc1-bdcbb5b9480a · outbound

This paper cites Pyramid scene parsing network.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Pyramid scene parsing network

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.200124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4cf155b-f3e0-4f41-92bb-0117f4ff1ed0 · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Unpaired image-to-image translation using cycle- consistent adversarial networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.189263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50082e2f-26d1-4e93-a20a-fc9583a01790 · outbound

This paper cites Penalizing top performers: Conservative loss for semantic segmentation adaptation.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Penalizing top performers: Conservative loss for semantic segmentation adaptation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.179527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55835d4b-61b2-48d3-b9d0-4af922c97be2 · outbound

This paper cites Target-driven vi- sual navigation in indoor scenes using deep reinforcement learning.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Target-driven vi- sual navigation in indoor scenes using deep reinforcement learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:39:20.169522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cfad57de-6c35-4e41-9c4a-8a5fb1709c5f · outbound

This paper cites Random” stands for the styles randomly selected from ImageNet and Artworks, and “Semantics.

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data Random” stands for the styles randomly selected from ImageNet and Artworks, and “Semantics

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T05:39:20.159054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Reference resolution

Outbound references

Pith citing papers