UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation | Paper Citation Record

Observation 0f5a9be0-7488-4a8c-9b62-45394b44526c · outbound

This paper cites Real-time monocular depth estimation using synthetic data with do- main adaptation via image style transfer.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Real-time monocular depth estimation using synthetic data with do- main adaptation via image style transfer

Reference 1

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Observation 01d470ff-f265-45f5-b47e-51a7e0631570 · outbound

This paper cites Unsupervised pixel- level domain adaptation with generative adversarial net- works.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unsupervised pixel- level domain adaptation with generative adversarial net- works

Reference 2

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Observation 956c0a5a-c6ad-4425-ab35-27f8c3767910 · outbound

This paper cites Multitask learning.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Multitask learning

Reference 3

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Observation 1b278b5b-4615-45b1-be0b-d131bca4c9c0 · outbound

This paper cites No more discrimi- nation: Cross city adaptation of road scene segmenters.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation No more discrimi- nation: Cross city adaptation of road scene segmenters

Reference 4

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Observation 882f78db-b678-4719-9e8a-c59fc6a6a4ef · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation The cityscapes dataset for semantic urban scene understanding

Reference 5

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Observation 193eda8f-0bb2-4f1c-b2b6-ae42be878557 · outbound

This paper cites Domain Adaptation for Visual Applications: A Comprehensive Survey.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Domain Adaptation for Visual Applications: A Comprehensive Survey

Reference 6

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Observation 90f9d56c-b1c1-43be-a11c-75fef7d0b1d7 · outbound

This paper cites Multi-task self- supervised visual learning.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Multi-task self- supervised visual learning

Reference 7

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Observation 76a932db-dba8-4649-b9c1-f77862643ec0 · outbound

This paper cites Predicting depth, surface nor- mals and semantic labels with a common multi-scale convo- lutional architecture.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Predicting depth, surface nor- mals and semantic labels with a common multi-scale convo- lutional architecture

Reference 8

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Observation 6efec788-a4eb-495a-987f-7f6ee15bfdfd · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Depth map prediction from a single image using a multi-scale deep net- work

Reference 9

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Observation 73b719b8-3013-432e-a33d-2f1f7b9bcded · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unsupervised domain adaptation by backpropagation

Reference 10

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Observation 5ecfc014-ead0-427d-8ae4-f9fbb6ee82a0 · outbound

This paper cites Domain-adversarial train- ing of neural networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Domain-adversarial train- ing of neural networks

Reference 11

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Observation 7782b6df-d088-4864-b7d3-48a31fd8e7a7 · outbound

This paper cites Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi

Reference 12

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Observation f9d1a494-895f-4d37-b068-f9c30231078d · outbound

This paper cites Bros- tow.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Bros- tow

Reference 13

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Observation a794d679-470f-4944-9263-1e0a9d17dc53 · outbound

This paper cites Generative adversarial nets.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Generative adversarial nets

Reference 14

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Observation 5e043755-b66f-4d20-90d8-e7e14441f79b · outbound

This paper cites Gretton, AJ.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Gretton, AJ

Reference 15

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Observation c1859283-9174-4afb-8e56-ec83a2fb4674 · outbound

This paper cites Heterogeneous face attribute estimation: A deep multi-task learning approach.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Heterogeneous face attribute estimation: A deep multi-task learning approach

Reference 16

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Observation 3fb3f8db-ad24-4d13-8f1d-74d8cc90f5e3 · outbound

This paper cites A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Reference 17

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Observation 40baecdd-e9db-4b91-8ff6-0a6b9317ad63 · outbound

This paper cites Distilling the knowledge in a neural network.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Distilling the knowledge in a neural network

Reference 18

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Observation 3940561f-ac7f-4dad-ae65-cfc13ce549b8 · outbound

This paper cites Cycada: Cycle-consistent adversarial domain adapta- tion.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Cycada: Cycle-consistent adversarial domain adapta- tion

Reference 19

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Observation 1b593950-7244-4f3d-8767-2653713ed5de · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 20

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Observation 80dbd298-1c05-4daf-b127-ab72bafc80c6 · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Conditional generative adversarial network for struc- tured domain adaptation

Reference 21

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Observation 4a9dae23-053f-4fb2-a287-e6282c31d86c · outbound

This paper cites an unresolved cited work.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unresolved cited work

Reference 22

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Observation 6bf36d0a-b1f7-4d26-9b50-e6f64c62d395 · outbound

This paper cites Analyzing modu- lar cnn architectures for joint depth prediction and semantic segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Analyzing modu- lar cnn architectures for joint depth prediction and semantic segmentation

Reference 23

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Observation 893c8f5a-cbaa-41fb-bed0-c0f273569847 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 24

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Observation fbcb04f5-3754-470a-866d-a1c44f25cb49 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Adam: A Method for Stochastic Optimization

Reference 25

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Observation 5ad5848a-de61-4bd3-90de-a1677bc5c72d · outbound

This paper cites Recurrent Scene Parsing with Perspective Understanding in the Loop.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Recurrent Scene Parsing with Perspective Understanding in the Loop

Reference 26

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Observation 6ddffa48-6f39-440c-aad0-2617633d45ef · outbound

This paper cites Saliency uniﬁed: A deep architecture for simultaneous eye ﬁxation prediction and salient object segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Saliency uniﬁed: A deep architecture for simultaneous eye ﬁxation prediction and salient object segmentation

Reference 27

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Observation afabff43-712f-4521-a4f3-7fcdee512663 · outbound

This paper cites Unsupervised feature learning of human actions as trajectories in pose embedding manifold.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unsupervised feature learning of human actions as trajectories in pose embedding manifold

Reference 28

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Observation 30f609c9-2cb1-4c8a-9445-ec5ab08be0d2 · outbound

This paper cites Adadepth: Unsupervised content congruent adaptation for depth estimation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Adadepth: Unsupervised content congruent adaptation for depth estimation

Reference 29

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Observation fd2ff583-42bc-4a40-a423-1aee7b30b425 · outbound

This paper cites Deeper depth prediction with fully convolutional residual networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Deeper depth prediction with fully convolutional residual networks

Reference 30

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Observation 574fb21b-81bc-4ed5-b735-9746919d1570 · outbound

This paper cites Reﬁnenet: Multi-path reﬁnement networks for high- resolution semantic segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Reﬁnenet: Multi-path reﬁnement networks for high- resolution semantic segmentation

Reference 31

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Observation e8c75af6-8fc4-4305-813a-94251711a4c3 · outbound

This paper cites Efﬁcient piecewise training of deep structured models for semantic segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Efﬁcient piecewise training of deep structured models for semantic segmentation

Reference 32

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Observation 471317d3-3bb7-43a7-b7ed-1b4982ba5234 · outbound

This paper cites Deep con- volutional neural ﬁelds for depth estimation from a single image.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Deep con- volutional neural ﬁelds for depth estimation from a single image

Reference 33

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Observation 18926ff1-ab6e-4a34-a215-87ef4c5efb9e · outbound

This paper cites End-to-End Multi-Task Learning with Attention.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation End-to-End Multi-Task Learning with Attention

Reference 34

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Observation 900a1d59-0854-4eb6-83dc-7a8591706ba0 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Fully convolutional networks for semantic segmentation

Reference 35

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Observation f8c15ae6-2c5e-4e0c-b7d5-f8ba9b10096f · outbound

This paper cites Learning transferable features with deep adaptation net- works.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Learning transferable features with deep adaptation net- works

Reference 36

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Observation 574d8f4d-444f-45bf-b437-f47a7ba70890 · outbound

This paper cites Unsupervised domain adaptation with residual trans- fer networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unsupervised domain adaptation with residual trans- fer networks

Reference 37

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Observation 417a7215-09a8-40a3-899e-1546bc2749e5 · outbound

This paper cites Cross-stitch networks for multi-task learning.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Cross-stitch networks for multi-task learning

Reference 38

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Observation db7cb88c-e6ed-4ade-a2c5-02c267ac1b09 · outbound

This paper cites Joint semantic segmentation and depth estimation with deep convolutional networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Joint semantic segmentation and depth estimation with deep convolutional networks

Reference 39

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Observation e24be3df-9dd3-45f9-b50a-7ed9320a84e0 · outbound

This paper cites Image to image translation for domain adaptation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Image to image translation for domain adaptation

Reference 40

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Observation b9e86d2d-9b4c-452b-b6a4-1286949a7aa9 · outbound

This paper cites Learning features by watching ob- jects move.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Learning features by watching ob- jects move

Reference 41

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Observation 7200c1ac-bbd8-4692-bd2a-be52656210c6 · outbound

This paper cites Geonet: Geometric neural network for joint depth and surface normal estimation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Geonet: Geometric neural network for joint depth and surface normal estimation

Reference 42

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Observation aa4ef38c-6f74-4d5f-ad44-ce8a33b46be2 · outbound

This paper cites Hy- perface: A deep multi-task learning framework for face de- tection, landmark localization, pose estimation, and gender recognition.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Hy- perface: A deep multi-task learning framework for face de- tection, landmark localization, pose estimation, and gender recognition

Reference 43

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Observation ccb05152-23ab-47e0-a1bc-173f106d0d8c · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 44

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Observation 8bee40b3-70d3-4b6f-9070-d63d55c828c9 · outbound

This paper cites Cross-domain self- supervised multi-task feature learning using synthetic im- agery.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Cross-domain self- supervised multi-task feature learning using synthetic im- agery

Reference 45

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Observation 66e7fc1b-066f-44a7-ad19-9c65cff8b4b4 · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Playing for data: Ground truth from computer games

Reference 46

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Observation 90ebf9ea-c2ef-4a45-8d84-4ed39b45f75c · outbound

This paper cites Monocular depth esti- mation using neural regression forest.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Monocular depth esti- mation using neural regression forest

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.626099Z

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Observation 866753d8-2259-494d-94d5-19521fdea0ca · outbound

This paper cites Imagenet large scale visual recognition challenge.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Imagenet large scale visual recognition challenge

Reference 48

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Observation dd7252e3-9077-43a9-a10c-059bdce88d85 · outbound

This paper cites Improved techniques for training gans.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Improved techniques for training gans

Reference 49

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Observation 1dd2d662-dd53-4296-b436-364b04f6b2f4 · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Learning from synthetic data: Addressing domain shift for semantic segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.595727Z

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Observation 8d8f994a-290f-4d69-9903-6c1ea4b47efa · outbound

This paper cites Make3d: Learning 3d scene structure from a single still image.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Make3d: Learning 3d scene structure from a single still image

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.584792Z

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This paper cites Indoor segmentation and support inference from rgbd images.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Indoor segmentation and support inference from rgbd images

Reference 52

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Observation 3ac025c7-ad39-43c7-baff-5b3f3d920f82 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Deep coral: Correlation alignment for deep domain adaptation

Reference 53

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This paper cites Tsai, W.-C.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Tsai, W.-C

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.558782Z

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This paper cites Simultaneous deep transfer across domains and tasks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Simultaneous deep transfer across domains and tasks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.549325Z

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Observation a27a9f9b-3848-4c4a-b692-cefab0dc4416 · outbound

This paper cites Adversarial discriminative domain adaptation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Adversarial discriminative domain adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.539477Z

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Observation bc3fc5eb-eee6-4573-a9b5-6944722ffaef · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Deep Domain Confusion: Maximizing for Domain Invariance

Reference 57

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Observation 8ff0a676-1473-4d84-9ceb-efb461ad69f1 · outbound

This paper cites Towards uniﬁed depth and seman- tic prediction from a single image.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Towards uniﬁed depth and seman- tic prediction from a single image

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.528935Z

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Observation e1f17ccc-5c15-4f67-9f19-04feb146c44b · outbound

This paper cites Design- ing deep networks for surface normal estimation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Design- ing deep networks for surface normal estimation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.519005Z

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Observation 9aee02a3-2b3b-4241-ac28-cee5b9010593 · outbound

This paper cites Holistically-nested edge detection.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Holistically-nested edge detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.508032Z

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Observation a9b8e8d8-8c31-4b70-90a5-cc21d5121a6f · outbound

This paper cites Describing the scene as a whole: Joint object detection, scene classiﬁcation and semantic segmentation.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Describing the scene as a whole: Joint object detection, scene classiﬁcation and semantic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.497352Z

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Observation 3626fb3e-7509-48ef-a97e-ea8cd23a3b64 · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Curricu- lum domain adaptation for semantic segmentation of urban scenes

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.486618Z

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Observation 6f47f890-a75e-403f-be12-d21b3c7550ba · outbound

This paper cites Physically-based rendering for indoor scene understanding using convolutional neural networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Physically-based rendering for indoor scene understanding using convolutional neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.476146Z

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Observation 9f50cc5a-372d-416e-a456-13004917e4ab · outbound

This paper cites Energy- based generative adversarial network.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Energy- based generative adversarial network

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.465216Z

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Observation b4ce2321-2b23-4c4a-aa52-6d01b65d4fc7 · outbound

This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.453831Z

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Observation df5333ef-8059-415e-9fe7-692b07bac571 · outbound

This paper cites Unsupervised learning of depth and ego-motion from video.

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unsupervised learning of depth and ego-motion from video

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:03:59.442762Z

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Observation bbc7ec7d-8ba9-4414-9329-a2532499cb66 · outbound

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

UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Unpaired image-to-image translation using cycle- consistent adversarial networks

Reference 67

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