NetTailor: Tuning the Architecture, Not Just the Weights | Paper Citation Record

Observation 48e05cde-b385-4bf4-8b21-9cab325fa880 · outbound

This paper cites Expert gate: Lifelong learning with a network of experts.

NetTailor: Tuning the Architecture, Not Just the Weights Expert gate: Lifelong learning with a network of experts

Reference 1

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Observation 65ed71f0-64e4-400e-9a3b-19f3924810ab · outbound

This paper cites Do deep nets really need to be deep? In Advances in Neural Information Processing Systems (NeurIPS).

NetTailor: Tuning the Architecture, Not Just the Weights Do deep nets really need to be deep? In Advances in Neural Information Processing Systems (NeurIPS)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.117472Z

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Observation b7a93167-7432-48ae-aac2-8978b91db702 · outbound

This paper cites Curriculum learning.

NetTailor: Tuning the Architecture, Not Just the Weights Curriculum learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.113884Z

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Observation 7a54ce0f-7f1f-4862-a0e5-a4fd681d3ade · outbound

This paper cites Model compression.

NetTailor: Tuning the Architecture, Not Just the Weights Model compression

Reference 4

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Observation 0be40f78-8b70-408b-8fd8-b62ef56574f4 · outbound

This paper cites Learning complexity-aware cascades for deep pedestrian detection.

NetTailor: Tuning the Architecture, Not Just the Weights Learning complexity-aware cascades for deep pedestrian detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.110664Z

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Observation b363ac51-0966-4c7a-8f11-ec1e6076b755 · outbound

This paper cites Multitask learning.

NetTailor: Tuning the Architecture, Not Just the Weights Multitask learning

Reference 6

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Observation 90988fc7-2396-4064-b4f1-a4388c4c10cf · outbound

This paper cites Net2Net: Accelerating Learning via Knowledge Transfer.

NetTailor: Tuning the Architecture, Not Just the Weights Net2Net: Accelerating Learning via Knowledge Transfer

Reference 7

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Observation af882ce8-7fa6-4081-92a9-5402c57f2d76 · outbound

This paper cites Describing textures in the wild.

NetTailor: Tuning the Architecture, Not Just the Weights Describing textures in the wild

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.946936Z

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Observation 9af2e924-61e9-490e-aaf6-fbf3831e53e5 · outbound

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

NetTailor: Tuning the Architecture, Not Just the Weights Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.022568Z

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Observation f554ef34-8853-4dad-9bc3-381740ff63e5 · outbound

This paper cites How do humans sketch objects? ACM Trans.

NetTailor: Tuning the Architecture, Not Just the Weights How do humans sketch objects? ACM Trans

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.981143Z

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Observation 26addd81-f77c-4860-b928-3645fef5d283 · outbound

This paper cites Everingham, L.

NetTailor: Tuning the Architecture, Not Just the Weights Everingham, L

Reference 11

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Observation 1968912d-98e7-4b50-87d9-568f172e8c57 · outbound

This paper cites Learning to Teach.

NetTailor: Tuning the Architecture, Not Just the Weights Learning to Teach

Reference 12

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Observation a7cf161d-a41c-4e0d-ba50-7dc8bd5c3eee · outbound

This paper cites Spatially adaptive computation time for residual networks.

NetTailor: Tuning the Architecture, Not Just the Weights Spatially adaptive computation time for residual networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.956428Z

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Observation b37db891-95ae-42c8-9acd-9be762a5e1cf · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

NetTailor: Tuning the Architecture, Not Just the Weights Unsupervised domain adaptation by backpropagation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.104793Z

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Observation f4f275cd-d7b4-46a8-a3ed-bfce40014f1b · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

NetTailor: Tuning the Architecture, Not Just the Weights Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.065724Z

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Observation 0d3f0c16-5b79-4557-aa21-33d7be9d1186 · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

NetTailor: Tuning the Architecture, Not Just the Weights An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.665682Z

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Observation 78818911-2d26-4170-8e08-1890bbe11086 · outbound

This paper cites Learning both weights and connections for efficient neural network.

NetTailor: Tuning the Architecture, Not Just the Weights Learning both weights and connections for efficient neural network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.974535Z

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Observation e24f9d3e-2970-4955-89b7-8c193be17ef9 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.

NetTailor: Tuning the Architecture, Not Just the Weights Second order derivatives for network pruning: Optimal brain surgeon

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.990678Z

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Observation 9b9de255-05b3-44a9-a950-d3b275d12643 · outbound

This paper cites Mask r-cnn.

NetTailor: Tuning the Architecture, Not Just the Weights Mask r-cnn

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.935858Z

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Observation 5ff7e998-60f7-43c4-9365-77f3b0f9a193 · outbound

This paper cites Deep residual learning for image recognition.

NetTailor: Tuning the Architecture, Not Just the Weights Deep residual learning for image recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.938889Z

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Observation 17102948-cd7b-4563-af4c-f93debffddad · outbound

This paper cites Distilling the Knowledge in a Neural Network.

NetTailor: Tuning the Architecture, Not Just the Weights Distilling the Knowledge in a Neural Network

Reference 21

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Observation 9954ff36-3273-436f-b294-01df7f0be6bd · outbound

This paper cites Multi-scale dense net- works for resource efficient image classification.

NetTailor: Tuning the Architecture, Not Just the Weights Multi-scale dense net- works for resource efficient image classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.943679Z

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Observation a02de8bc-533b-40bb-bd74-c00dde530b99 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and seman- tics.

NetTailor: Tuning the Architecture, Not Just the Weights Multi-task learning using uncertainty to weigh losses for scene geometry and seman- tics

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.996815Z

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Observation 8e80b8c1-6fc4-49f9-b019-5cd3b0eedb2e · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.National Academy of Sciences.

NetTailor: Tuning the Architecture, Not Just the Weights Overcoming catastrophic forgetting in neural networks.National Academy of Sciences

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.924403Z

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Observation 9d621d1d-7215-47ff-8677-9caf7168cfa6 · outbound

This paper cites 3D object representations for fine-grained categorization.

NetTailor: Tuning the Architecture, Not Just the Weights 3D object representations for fine-grained categorization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.930532Z

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Observation 74297234-1285-4848-8cb3-3bc461668e5b · outbound

This paper cites Learning multiple layers of features from tiny images.

NetTailor: Tuning the Architecture, Not Just the Weights Learning multiple layers of features from tiny images

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.933256Z

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Observation 0461661b-9f62-4a1b-b61a-70d0d2916214 · outbound

This paper cites Im- agenet classification with deep convolutional neural networks.

NetTailor: Tuning the Architecture, Not Just the Weights Im- agenet classification with deep convolutional neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.006134Z

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Observation 7ad22ce2-0187-472c-8df1-0b691a6ea317 · outbound

This paper cites Human-level concept learning through probabilistic program induction.

NetTailor: Tuning the Architecture, Not Just the Weights Human-level concept learning through probabilistic program induction

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.058203Z

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Observation 395b9a52-9519-4af8-861e-f3d6d36e18b6 · outbound

This paper cites Optimal brain damage.

NetTailor: Tuning the Architecture, Not Just the Weights Optimal brain damage

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.050354Z

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Observation c47d7c10-fdb4-4ceb-b315-54b988c04642 · outbound

This paper cites Overcoming catastrophic forgetting by incremental moment matching.

NetTailor: Tuning the Architecture, Not Just the Weights Overcoming catastrophic forgetting by incremental moment matching

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.088950Z

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Observation eea1ef0c-e98d-4ab4-8c04-71cc48223284 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

NetTailor: Tuning the Architecture, Not Just the Weights Pruning Filters for Efficient ConvNets

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.675177Z

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Observation 5546339f-b0e9-49c4-a0ac-9cb73ac48f4e · outbound

This paper cites Learning without forgetting.

NetTailor: Tuning the Architecture, Not Just the Weights Learning without forgetting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.085747Z

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Observation 80c1e539-fdf5-47ac-8431-0a4d7f898de2 · outbound

This paper cites Microsoft coco: Common objects in context.

NetTailor: Tuning the Architecture, Not Just the Weights Microsoft coco: Common objects in context

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.009754Z

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Observation 625fa44d-d99d-41ff-b0b2-fdb135583214 · outbound

This paper cites Progressive neural architecture search.

NetTailor: Tuning the Architecture, Not Just the Weights Progressive neural architecture search

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.002796Z

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Observation bcd1562e-2b05-48c5-8d75-f1223478919e · outbound

This paper cites DARTS: Differentiable Architecture Search.

NetTailor: Tuning the Architecture, Not Just the Weights DARTS: Differentiable Architecture Search

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.698616Z

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Observation 941beeff-faa3-4448-af03-f62837d84459 · outbound

This paper cites Gradient episodic memory for continual learning.

NetTailor: Tuning the Architecture, Not Just the Weights Gradient episodic memory for continual learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.953469Z

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Observation 9e5e2e39-c9f1-4be0-9078-5673befd0f3b · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

NetTailor: Tuning the Architecture, Not Just the Weights Fine-Grained Visual Classification of Aircraft

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.649521Z

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Observation 4ba5f8dc-d452-4ee7-88d8-d16611467873 · outbound

This paper cites Piggyback: Adapting a single network to multiple tasks by learning to mask weights.

NetTailor: Tuning the Architecture, Not Just the Weights Piggyback: Adapting a single network to multiple tasks by learning to mask weights

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.964423Z

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Observation 3ecd3cf1-c21b-46fe-88af-13e37aa034e5 · outbound

This paper cites Packnet: Adding multiple tasks to a single network by iterative pruning.

NetTailor: Tuning the Architecture, Not Just the Weights Packnet: Adding multiple tasks to a single network by iterative pruning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.971572Z

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Observation adc94403-5809-4759-b43a-bd17d5056bdc · outbound

This paper cites Teacher-Student Curriculum Learning.

NetTailor: Tuning the Architecture, Not Just the Weights Teacher-Student Curriculum Learning

Reference 40

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Observation 42a96e4f-adf7-4d51-9838-c5530146a29e · outbound

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

NetTailor: Tuning the Architecture, Not Just the Weights Cross-stitch networks for multi-task learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.977699Z

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Observation 95c28258-4129-4e46-9d09-8a165bae5bd7 · outbound

This paper cites Never-ending learning.

NetTailor: Tuning the Architecture, Not Just the Weights Never-ending learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.920672Z

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Observation 929634ee-009a-4c2f-a8f5-47aa1a0d10fe · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

NetTailor: Tuning the Architecture, Not Just the Weights Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.682221Z

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Observation 2d0d1c6b-87ef-4aa5-8598-b56529896d0c · outbound

This paper cites An experimental study on pedestrian classification.IEEE Transactions on P attern Analysis and Machine Intelligence (TP AMI), 28(11):1863–1868.

NetTailor: Tuning the Architecture, Not Just the Weights An experimental study on pedestrian classification.IEEE Transactions on P attern Analysis and Machine Intelligence (TP AMI), 28(11):1863–1868

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.959287Z

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Observation 7aa371b2-16d1-4bf9-8b2f-84c1f48746e1 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

NetTailor: Tuning the Architecture, Not Just the Weights Reading digits in natural images with unsupervised feature learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.072333Z

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Observation 4b702c5a-8fd4-467d-bbfe-66344e8bab91 · outbound

This paper cites Nilsback and A.

NetTailor: Tuning the Architecture, Not Just the Weights Nilsback and A

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.987718Z

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Observation 256f44cf-bc21-44a2-a1aa-efecb3a20318 · outbound

This paper cites Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition.

NetTailor: Tuning the Architecture, Not Just the Weights Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.927452Z

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Observation 120fdc55-62cd-4a75-a49d-908905efea74 · outbound

This paper cites Encoder based lifelong learning.

NetTailor: Tuning the Architecture, Not Just the Weights Encoder based lifelong learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.040519Z

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Observation 1a2effc8-e03c-4c57-8bbb-92b1b6bb00a4 · outbound

This paper cites Learning multiple visual domains with residual adapters.

NetTailor: Tuning the Architecture, Not Just the Weights Learning multiple visual domains with residual adapters

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.075477Z

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Observation cbc06585-8933-4450-a4c6-fa43e00ee7e2 · outbound

This paper cites Efficient parametrization of multi-domain deep neural networks.

NetTailor: Tuning the Architecture, Not Just the Weights Efficient parametrization of multi-domain deep neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.917502Z

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Observation f5b153ae-58b2-474c-a6c4-9f4f609ba53f · outbound

This paper cites icarl: Incremental classifier and representation learning.

NetTailor: Tuning the Architecture, Not Just the Weights icarl: Incremental classifier and representation learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.950558Z

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Observation 25f9d0a6-f9dc-4bbf-9f0c-83d94073f7d1 · outbound

This paper cites Y ou only look once: Unified, real-time object detection.

NetTailor: Tuning the Architecture, Not Just the Weights Y ou only look once: Unified, real-time object detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.967852Z

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Observation ac3f63dc-174a-4ae7-9c95-e103a3efd633 · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

NetTailor: Tuning the Architecture, Not Just the Weights FitNets: Hints for Thin Deep Nets

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.669539Z

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Observation 398118dd-c2e5-45ce-9308-91e4dc22d64a · outbound

This paper cites Incremental learning through deep adaptation.IEEE Transactions on P attern Analysis and Machine Intelligence (P AMI).

NetTailor: Tuning the Architecture, Not Just the Weights Incremental learning through deep adaptation.IEEE Transactions on P attern Analysis and Machine Intelligence (P AMI)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.029755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ab6b5329-f721-4356-b85a-9e777b471203 · outbound

This paper cites Progressive Neural Networks.

NetTailor: Tuning the Architecture, Not Just the Weights Progressive Neural Networks

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.644513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 002ef82a-8647-4c1a-ae1d-b6bb3c62ba25 · outbound

This paper cites Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature.

NetTailor: Tuning the Architecture, Not Just the Weights Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.640268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 48d479b4-4b46-4ae0-9697-cc7ae6e2c0a9 · outbound

This paper cites Facenet: A unified embedding for face recognition and clustering.

NetTailor: Tuning the Architecture, Not Just the Weights Facenet: A unified embedding for face recognition and clustering

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.914402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6406c48-441c-4626-8924-695c95459eec · outbound

This paper cites Cnn features off-the-shelf: an astounding baseline for recognition.

NetTailor: Tuning the Architecture, Not Just the Weights Cnn features off-the-shelf: an astounding baseline for recognition

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.054996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 44431afd-61e0-4962-863a-cc18e862076c · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

NetTailor: Tuning the Architecture, Not Just the Weights UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.686434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f33dafb1-dda1-4dd0-844c-0800d2c31a44 · outbound

This paper cites an unresolved cited work.

NetTailor: Tuning the Architecture, Not Just the Weights Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-25T12:36:57.993408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2d746eff-d01a-401c-9ed9-54af29196c25 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

NetTailor: Tuning the Architecture, Not Just the Weights Branchynet: Fast inference via early exiting from deep neural networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:57.984438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2f059af4-ad1c-48a5-917f-4bca638392e4 · outbound

This paper cites A lifelong learning perspective for mobile robot control.

NetTailor: Tuning the Architecture, Not Just the Weights A lifelong learning perspective for mobile robot control

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.096416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1034914a-22e5-4df8-943f-421de4186c96 · outbound

This paper cites Simultaneous deep transfer across domains and tasks.

NetTailor: Tuning the Architecture, Not Just the Weights Simultaneous deep transfer across domains and tasks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.082255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0238a744-22b7-4710-8427-cbfd1530c444 · outbound

This paper cites Adversarial discriminative domain adaptation.

NetTailor: Tuning the Architecture, Not Just the Weights Adversarial discriminative domain adaptation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.061976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e567e426-73f9-4048-9c56-60156a7d13dd · outbound

This paper cites Convolutional networks with adaptive inference graphs.

NetTailor: Tuning the Architecture, Not Just the Weights Convolutional networks with adaptive inference graphs

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.079182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1e23c72c-430b-4c82-b71d-3fbb0785e33c · outbound

This paper cites Rapid object detection using a boosted cascade of simple features.

NetTailor: Tuning the Architecture, Not Just the Weights Rapid object detection using a boosted cascade of simple features

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.101154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 14953e0c-2100-4deb-9d88-88c95ed2f33c · outbound

This paper cites an unresolved cited work.

NetTailor: Tuning the Architecture, Not Just the Weights Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-05-25T12:36:58.043524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6b8f01ef-8733-4d3a-b76d-1c95d808bc7b · outbound

This paper cites Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers.

NetTailor: Tuning the Architecture, Not Just the Weights Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.037525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dc7930ec-e4d9-4344-b197-afe1d9b6d90c · outbound

This paper cites Lifelong Learning with Dynamically Expandable Networks.

NetTailor: Tuning the Architecture, Not Just the Weights Lifelong Learning with Dynamically Expandable Networks

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.694835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation df47a911-4b36-4910-ac50-c0d9dc543257 · outbound

This paper cites How transferable are features in deep neural networks? InAdvances in Neural Information Processing Systems (NeurIPS).

NetTailor: Tuning the Architecture, Not Just the Weights How transferable are features in deep neural networks? InAdvances in Neural Information Processing Systems (NeurIPS)

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.034162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 63efa0dc-a185-4b8e-8b1f-d1bfb051c4b7 · outbound

This paper cites Wide residual networks.

NetTailor: Tuning the Architecture, Not Just the Weights Wide residual networks

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.015943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e0045f71-47c5-4f50-85ce-175265496a03 · outbound

This paper cites Facial landmark detection by deep multi-task learning.

NetTailor: Tuning the Architecture, Not Just the Weights Facial landmark detection by deep multi-task learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.019583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c7547a1e-fae2-4b3a-82e3-a17c77ffb53b · outbound

This paper cites Learning deep features for scene recognition using places database.

NetTailor: Tuning the Architecture, Not Just the Weights Learning deep features for scene recognition using places database

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.025938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation de1486fb-49ef-41ac-8f06-d063e5ffab61 · outbound

This paper cites Less is more: T owards compact cnns.

NetTailor: Tuning the Architecture, Not Just the Weights Less is more: T owards compact cnns

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.012969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a3ef7730-0c81-453c-9240-a0e67e0477da · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

NetTailor: Tuning the Architecture, Not Just the Weights Neural Architecture Search with Reinforcement Learning

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.631634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7d86da89-f95f-484e-a667-8a53437abf25 · outbound

This paper cites Learning Transferable Architectures for Scalable Image Recognition.

NetTailor: Tuning the Architecture, Not Just the Weights Learning Transferable Architectures for Scalable Image Recognition

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.636021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Reference resolution

Outbound references

Pith citing papers