Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement | Paper Citation Record

Observation 25ab7421-bded-45b6-a42f-ecd696440e88 · outbound

This paper cites Im- agenet: A large-scale hierarchical image database,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Im- agenet: A large-scale hierarchical image database,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.402456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6db7a6d8-c71b-4e3e-a80f-002b70393b85 · outbound

This paper cites Openimages: A public dataset for large-scale multi-label and multi-class image classiﬁcation.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Openimages: A public dataset for large-scale multi-label and multi-class image classiﬁcation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.524701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8f4804c5-b148-4f02-8213-e92a081c3e6a · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Dropout: a simple way to prevent neural networks from overﬁtting

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.499725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0ca15cc-df91-4f2a-9838-7c6277ef5eac · outbound

This paper cites Regu- larization of neural networks using dropconnect,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Regu- larization of neural networks using dropconnect,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.477499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ae50b3a-a203-417d-a9a5-fb82d5a9f978 · outbound

This paper cites Deep networks with stochastic depth,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Deep networks with stochastic depth,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.448390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d90b0c87-5fe1-4310-8a87-a10fbd6bb3fe · outbound

This paper cites Swapout: Learning an ensemble of deep architectures,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Swapout: Learning an ensemble of deep architectures,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.415705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29710ac7-675e-40f4-bc32-16329d61b7ba · outbound

This paper cites Visualizing data using t-sne,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Visualizing data using t-sne,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.441446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17fea7c3-71b1-477a-b70d-28efa5968c90 · outbound

This paper cites Shake-Shake regularization.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Shake-Shake regularization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.447949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 319ee813-7564-468e-b47d-e1307ce36f8a · outbound

This paper cites Meal: Multi-model ensemble via adversarial learning,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Meal: Multi-model ensemble via adversarial learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.371312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e812c425-e196-41cc-975b-7ee56f495865 · outbound

This paper cites Snapshot ensembles: Train 1, get m for free,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Snapshot ensembles: Train 1, get m for free,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.344339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6ea0a760-9cbd-4be8-8741-d5c14d865344 · outbound

This paper cites Neural network ensembles,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Neural network ensembles,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.322829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6cdae2a4-5bff-46b4-bbc6-cc399aceafe7 · outbound

This paper cites When networks disagree: En- semble methods for hybrid neural networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement When networks disagree: En- semble methods for hybrid neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.298032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a2da5ff-619c-4842-a21c-8bff7ae9c5fc · outbound

This paper cites Neural network ensembles, cross validation, and active learning,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Neural network ensembles, cross validation, and active learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.275129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4e4f725b-8fde-4dbf-b0ba-9b73787f7199 · outbound

This paper cites Ensemble methods in machine learning,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Ensemble methods in machine learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.251735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 386b7b20-4006-4223-b64a-2570dbf9770b · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.227058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 053427cb-135c-4a5f-9952-ac8367cc7962 · outbound

This paper cites Knowledge distillation by on-the-ﬂy native ensemble,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Knowledge distillation by on-the-ﬂy native ensemble,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.205239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 128f71b3-fee0-436a-b0e6-2d7af9127ccb · outbound

This paper cites Diverse ensemble evolution: Curriculum data-model marriage,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Diverse ensemble evolution: Curriculum data-model marriage,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.180837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3cc22b1d-40c7-4384-9831-0d0b34d6061b · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Distilling the Knowledge in a Neural Network

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.507206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49011a91-b2cd-4c30-a936-1c607307585d · outbound

This paper cites Semi-supervised knowledge transfer for deep learning from private training data,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Semi-supervised knowledge transfer for deep learning from private training data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.149180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1c7aba55-2b46-4840-bb70-21a1047a6e9d · outbound

This paper cites Learning from noisy labels with distillation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning from noisy labels with distillation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.122142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60f8b062-03ed-49c3-a56f-43ffd624cdfa · outbound

This paper cites A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.095009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6bea4c14-0adb-4a18-aed5-6ecdb9ca6931 · outbound

This paper cites Generative adversarial nets,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Generative adversarial nets,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.066127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ea6e9b6f-13b6-4ace-a714-2fed3319b129 · outbound

This paper cites Label reﬁnery: Improving imagenet classiﬁcation through label progression,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Label reﬁnery: Improving imagenet classiﬁcation through label progression,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.043851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d81bb5c0-171d-4d0e-b51f-91374c6d50b5 · outbound

This paper cites Wasserstein generative adversarial networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Wasserstein generative adversarial networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:07.021678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e3af3db3-75af-45f1-bb46-564ce4e1973c · outbound

This paper cites Improved training of wasserstein gans,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Improved training of wasserstein gans,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.999517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 291e09c3-bcae-48ef-abd1-7c41f7876e50 · outbound

This paper cites On Convergence and Stability of GANs.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement On Convergence and Stability of GANs

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.563547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c7f01b0-bce0-486f-875c-e146608cdc0c · outbound

This paper cites Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.569147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8dcc34-025e-4aba-831d-f117e8fb30f5 · outbound

This paper cites Least squares generative adversarial networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Least squares generative adversarial networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.977909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5600b450-5af9-4c42-b5cb-1655a48d19bc · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Image-to-image translation with conditional adversarial networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.954220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 58ae4e6f-9df6-4e6b-9de2-5e36d7ec24ed · outbound

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

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Unpaired image-to- image translation using cycle-consistent adversarial networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.934125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b25e86db-6ebf-4265-b098-2b885a6169ad · outbound

This paper cites Toward multimodal image-to-image translation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Toward multimodal image-to-image translation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.911136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 04c93e3d-8f06-4d3a-bd26-5330b6f416e6 · outbound

This paper cites Unsupervised image-to-image translation networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Unsupervised image-to-image translation networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.885463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4eb86805-3bdc-4338-849e-4491612d4091 · outbound

This paper cites Multimodal unsupervised image-to-image translation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Multimodal unsupervised image-to-image translation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.865879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22c5ad0a-ea6a-4639-90e5-0c7c15b3ec88 · outbound

This paper cites Towards instance-level image-to-image translation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Towards instance-level image-to-image translation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.844585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 468deefd-4efd-4940-8cab-ee0d587c686a · outbound

This paper cites Image generation from scene graphs,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Image generation from scene graphs,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.823335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d3742cef-a19e-4ccc-8b59-7bfefa1883d1 · outbound

This paper cites Finding tiny faces in the wild with generative adversarial network,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Finding tiny faces in the wild with generative adversarial network,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.802280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1746177a-cb80-46aa-a51f-05c15eda525e · outbound

This paper cites Net2net: Accelerating learning via knowledge transfer,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Net2net: Accelerating learning via knowledge transfer,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.781758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 34300875-1ae7-442f-8aa2-fefd7ee2bffe · outbound

This paper cites Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.625157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4726ad2-eb23-4f19-bbc8-78c72ae7cd8b · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.759036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09d04c5d-00de-4c09-bc04-e4de2186dca9 · outbound

This paper cites Large scale distributed neural network training through online distillation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Large scale distributed neural network training through online distillation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.726798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5bec2de2-a7d4-44a1-a518-8d0bcf571422 · outbound

This paper cites Darkrank: Accelerating deep metric learning via cross sample similarities transfer,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Darkrank: Accelerating deep metric learning via cross sample similarities transfer,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.706081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d6e9483-581f-427e-a0cf-38cb7352593c · outbound

This paper cites Relational knowledge distillation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Relational knowledge distillation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.687177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7cf23720-26bb-4ef3-aa59-10c35d785931 · outbound

This paper cites Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.651715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52546b84-cce6-4842-a41f-75dadb43269f · outbound

This paper cites Born Again Neural Networks.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Born Again Neural Networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.657793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e246503-d4aa-4286-8cc8-0e535e61e405 · outbound

This paper cites Revisiting knowledge transfer for training object class detectors,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Revisiting knowledge transfer for training object class detectors,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.668667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2297e50a-3c92-47a7-8592-f9c16b39cdd3 · outbound

This paper cites Moonshine: Distilling with cheap convolutions,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Moonshine: Distilling with cheap convolutions,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.647204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc29057b-03ae-4182-86f5-7e225d565c7c · outbound

This paper cites Snapshot distillation: Teacher-student optimization in one generation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Snapshot distillation: Teacher-student optimization in one generation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.624301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a884225b-5520-4bed-9209-560bfe442de6 · outbound

This paper cites Learning to learn from noisy labeled data,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning to learn from noisy labeled data,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.604565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4e963bbf-ec9f-432c-9ffc-db31cdc4e22d · outbound

This paper cites Toward robustness against label noise in training deep discriminative neural networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Toward robustness against label noise in training deep discriminative neural networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.586552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7b8cdec6-92c0-408e-8b6f-20f84c0b74f1 · outbound

This paper cites Learning from massive noisy labeled data for image classiﬁcation,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning from massive noisy labeled data for image classiﬁcation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.566727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9c880d4-18f8-488e-9feb-1fefcffb63e4 · outbound

This paper cites MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.695303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1446cf6-4e5e-434d-a00a-f7b1e7c86cb4 · outbound

This paper cites Training deep neural-networks using a noise adaptation layer,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Training deep neural-networks using a noise adaptation layer,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.547064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5af1930c-17f7-4b74-8703-0698087df9cd · outbound

This paper cites Learning from noisy large-scale datasets with minimal supervision,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning from noisy large-scale datasets with minimal supervision,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.525114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc8ad90a-6b66-4c08-b02d-8c709edc8d5f · outbound

This paper cites Training Convolutional Networks with Noisy Labels.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Training Convolutional Networks with Noisy Labels

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.710584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4acad293-1d19-4045-a393-6f4af633b6e7 · outbound

This paper cites Making deep neural networks robust to label noise: A loss correc- tion approach,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Making deep neural networks robust to label noise: A loss correc- tion approach,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.505130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3802cc0b-4f26-4b64-9f12-d3ae5f1ad21b · outbound

This paper cites Learning to Reweight Examples for Robust Deep Learning.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning to Reweight Examples for Robust Deep Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.721401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57136043-42fc-4c71-adf8-301c95850fcd · outbound

This paper cites Cleannet: Transfer learning for scalable image classiﬁer training with label noise,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Cleannet: Transfer learning for scalable image classiﬁer training with label noise,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.484530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0db50957-2826-45dd-b6fc-fa6b76cf9b59 · outbound

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

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Classiﬁcation with noisy labels by importance reweighting,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.464813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6615b52-6033-48ea-b030-c7d6d22de6af · outbound

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

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning multiple layers of features from tiny images,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.740592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03fb3cde-fb6a-4d4a-81fb-cf966b2691ab · outbound

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

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Reading digits in natural images with unsupervised feature learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.431028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a1fec6e-dbeb-4b7b-80ad-241eeabaa1bb · outbound

This paper cites Very deep convolutional net- works for large-scale image recognition,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Very deep convolutional net- works for large-scale image recognition,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.751412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3168f1f6-1991-4f8d-a8f6-28a7ae69c77c · outbound

This paper cites Deep residual learning for image recognition,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Deep residual learning for image recognition,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.757159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07eeae1f-bd32-41d7-be8e-cf2d6fd8fc7b · outbound

This paper cites Densely connected convolutional networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Densely connected convolutional networks,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.762566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fecf5f7b-4acb-475e-be7d-43b619db0a4e · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.768062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1864cf7-8e99-452f-8501-70249f5f0e93 · outbound

This paper cites Automatic differ- entiation in pytorch,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Automatic differ- entiation in pytorch,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.773707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f36b49f-72b3-445e-a9d9-f9ff5be6e1e5 · outbound

This paper cites Deeply-supervised nets.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Deeply-supervised nets

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.347179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 24e14ad7-fa1d-41e5-a6e4-1661245d044a · outbound

This paper cites Fitnets: Hints for thin deep nets,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Fitnets: Hints for thin deep nets,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.324562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 154605b1-5d6f-4f24-8f7b-fb26433f9ab3 · outbound

This paper cites FractalNet: Ultra-Deep Neural Networks without Residuals.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement FractalNet: Ultra-Deep Neural Networks without Residuals

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.790945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0530f8f-18fc-4436-8378-1377cd1a2443 · outbound

This paper cites Learning efﬁcient convolutional networks through network slimming,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Learning efﬁcient convolutional networks through network slimming,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.301406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a45a7ea4-de44-4d71-a5d6-4182bd45b721 · outbound

This paper cites Maxout networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Maxout networks,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.282847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 62deac91-5a8e-4dac-9135-a75c05f21e67 · outbound

This paper cites Imagenet classiﬁ- cation with deep convolutional neural networks,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Imagenet classiﬁ- cation with deep convolutional neural networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.265473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2180c811-aeb9-42bf-830e-9e57771a641d · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.814363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a80775-9c8f-4b65-a5d0-33c96575d03a · outbound

This paper cites Going deeper with convolutions,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement Going deeper with convolutions,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.247114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98f35d09-94ef-44cf-8e00-b9a10d0f3c36 · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement mixup: Beyond empirical risk minimization,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:42:06.227596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b0f64a8-ede5-48e1-8e5f-fd5b445f9220 · outbound

This paper cites CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.833857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4fc13b8-a58d-4271-9e32-278cd15bd0ea · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-14T11:42:05.840537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers