A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge | Paper Citation Record

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This paper cites Data augmentation for improving deep learning in image classification problem,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Data augmentation for improving deep learning in image classification problem,

Reference 1

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Capan: Class-aware prototypical adversarial net- works for unsupervised domain adaptation,

Reference 2

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Multi-batch nuclear-norm adversarial net- work for unsupervised domain adaptation,

Reference 3

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This paper cites Object detection with deep learning: A review,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Object detection with deep learning: A review,

Reference 4

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This paper cites Evaluation-oriented knowledge distillation for deep face recognition,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Evaluation-oriented knowledge distillation for deep face recognition,

Reference 5

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This paper cites Fast human pose estimation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Fast human pose estimation,

Reference 6

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This paper cites Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Semantics-aware adaptive knowledge distillation for sensor-to-vision action recognition,

Reference 7

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This paper cites A survey on deep learn- ing techniques for image and video semantic segmentation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge A survey on deep learn- ing techniques for image and video semantic segmentation,

Reference 8

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This paper cites Pixel representation augmented through cross-attention for high-resolution remote sensing imagery segmentation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Pixel representation augmented through cross-attention for high-resolution remote sensing imagery segmentation,

Reference 9

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This paper cites The Computational Limits of Deep Learning.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge The Computational Limits of Deep Learning

Reference 10

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This paper cites Compute-in-memory chips for deep learning: Recent trends and prospects,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Compute-in-memory chips for deep learning: Recent trends and prospects,

Reference 11

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This paper cites Energy and policy con- siderations for modern deep learning research,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Energy and policy con- siderations for modern deep learning research,

Reference 12

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This paper cites Edge-computing based knowledge distillation and multi- task learning for partial discharge recognition,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Edge-computing based knowledge distillation and multi- task learning for partial discharge recognition,

Reference 13

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This paper cites Dpal-bert: A faster and lighter ques- tion answering model.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Dpal-bert: A faster and lighter ques- tion answering model

Reference 14

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This paper cites Learning both weights and con- nections for efficient neural network,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Learning both weights and con- nections for efficient neural network,

Reference 15

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This paper cites Quantized convolutional neural networks for mobile devices,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Quantized convolutional neural networks for mobile devices,

Reference 16

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This paper cites Model compression,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Model compression,

Reference 17

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This paper cites Self-knowledge distillation with progressive refinement of targets,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Self-knowledge distillation with progressive refinement of targets,

Reference 18

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This paper cites Simplifying neural networks by soft weight sharing,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Simplifying neural networks by soft weight sharing,

Reference 19

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This paper cites Dropout: a simple way to prevent neural networks from over- fitting,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Dropout: a simple way to prevent neural networks from over- fitting,

Reference 20

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 21

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This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Improved Regularization of Convolutional Neural Networks with Cutout

Reference 22

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This paper cites Distilling the Knowledge in a Neural Network.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Distilling the Knowledge in a Neural Network

Reference 23

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This paper cites Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery

Reference 24

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This paper cites Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Reference 25

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This paper cites Hierarchical Self-supervised Augmented Knowledge Distillation.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Hierarchical Self-supervised Augmented Knowledge Distillation

Reference 26

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This paper cites Knowledge distillation using hierarchical self-supervision aug- mented distribution,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation using hierarchical self-supervision aug- mented distribution,

Reference 27

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This paper cites Knowledge distillation beyond model compression,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation beyond model compression,

Reference 28

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This paper cites ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation

Reference 29

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Constructing three-way decision with fuzzy granular-ball rough sets based on uncertainty invariance,

Reference 30

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This paper cites Unifying distillation and privileged information.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Unifying distillation and privileged information

Reference 31

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This paper cites Online knowledge distillation with diverse peers,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Online knowledge distillation with diverse peers,

Reference 32

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This paper cites Densely guided knowledge distillation using multiple teacher assistants,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Densely guided knowledge distillation using multiple teacher assistants,

Reference 33

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This paper cites Continual learning with knowl- edge distillation: A survey,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Continual learning with knowl- edge distillation: A survey,

Reference 34

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Dynamic contrastive distillation for image-text retrieval,

Reference 35

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This paper cites Knowledge distillation: A survey,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation: A survey,

Reference 36

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This paper cites A survey on large language models: Applications, challenges, limitations, and practical usage,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge A survey on large language models: Applications, challenges, limitations, and practical usage,

Reference 37

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge A Survey on Knowledge Distillation of Large Language Models

Reference 38

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge GPT-4 Technical Report

Reference 39

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 40

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This paper cites Mistral–a journey towards reproducible language model training,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Mistral–a journey towards reproducible language model training,

Reference 41

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This paper cites On the efficacy of knowledge distillation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge On the efficacy of knowledge distillation,

Reference 42

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This paper cites Curiosity-bottleneck: Exploration by distilling task-specific novelty,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Curiosity-bottleneck: Exploration by distilling task-specific novelty,

Reference 43

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This paper cites Learning efficient vision transformers via fine-grained manifold distil- lation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Learning efficient vision transformers via fine-grained manifold distil- lation,

Reference 44

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This paper cites Robust overfitting may be mitigated by properly learned smoothening,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Robust overfitting may be mitigated by properly learned smoothening,

Reference 45

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This paper cites Varia- tional information distillation for knowledge transfer,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Varia- tional information distillation for knowledge transfer,

Reference 46

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This paper cites Relational knowledge distilla- tion,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Relational knowledge distilla- tion,

Reference 47

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This paper cites Similarity-preserving knowledge distillation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Similarity-preserving knowledge distillation,

Reference 48

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

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Fitnets: Hints for thin deep nets,

Reference 49

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

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,

Reference 50

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This paper cites Knowledge transfer via distillation of activation boundaries formed by hidden neurons,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge transfer via distillation of activation boundaries formed by hidden neurons,

Reference 51

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This paper cites Deeply-supervised nets,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Deeply-supervised nets,

Reference 52

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This paper cites Knowl- edge distillation with the reused teacher classifier,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowl- edge distillation with the reused teacher classifier,

Reference 53

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This paper cites Knowledge distillation with deep su- pervision,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation with deep su- pervision,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:37.130283Z

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Observation 5e4647c8-ff2a-4816-9662-03e014e1e093 · outbound

This paper cites Contrastive Representation Distillation.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Contrastive Representation Distillation

Reference 55

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Observation 1a7442e8-d54f-4038-bae1-97d3ab6794f8 · outbound

This paper cites Sskd: Self- supervised knowledge distillation for cross domain adaptive person re- identification,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Sskd: Self- supervised knowledge distillation for cross domain adaptive person re- identification,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:37.117342Z

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Observation 1e5db6d0-b3ed-482b-91f4-74eebd616dad · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge A simple framework for contrastive learning of visual representations,

Reference 57

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Observation 8e424675-c69b-417e-95d2-3c6b65e52235 · outbound

This paper cites Revisiting knowledge distillation via label smoothing regularization,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Revisiting knowledge distillation via label smoothing regularization,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:37.095109Z

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Observation 0054ac38-ddd4-4f82-bfa7-b95f9395d423 · outbound

This paper cites Regularizing class-wise predictions via self-knowledge distillation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Regularizing class-wise predictions via self-knowledge distillation,

Reference 59

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Observation 5ae0e1a5-e4a0-4bbb-bcd0-a966c7a49d97 · outbound

This paper cites Scale fusion light cnn for hyperspectral face recognition with knowledge distillation and attention mechanism,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Scale fusion light cnn for hyperspectral face recognition with knowledge distillation and attention mechanism,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:36.698238Z

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Observation 805be5c2-7e8d-4b1a-b114-13a08df3d1c4 · outbound

This paper cites Knowledge distillation meets self- supervision,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation meets self- supervision,

Reference 61

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Observation 8c11f18b-8553-40ca-a4bf-afd9e91b0617 · outbound

This paper cites Learning from multiple teacher networks,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Learning from multiple teacher networks,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:36.107694Z

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Observation 8a527ea1-5b84-4c05-a780-5194bbdca6b7 · outbound

This paper cites Identity mappings in deep residual networks,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Identity mappings in deep residual networks,

Reference 63

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Observation dd26dde0-9ac7-4b92-9e2c-8d1702585534 · outbound

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

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Learning multiple layers of features from tiny images,

Reference 64

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This paper cites Imagenet classification with deep convolutional neural networks,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Imagenet classification with deep convolutional neural networks,

Reference 65

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Observation 81866a1d-8f44-46b1-928a-ea638bbebe91 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Tiny imagenet visual recognition challenge,

Reference 66

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This paper cites Deep residual learning for image recognition,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Deep residual learning for image recognition,

Reference 67

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Observation 9389b985-060f-43a6-9831-c8e582d577e1 · outbound

This paper cites Wide Residual Networks.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Wide Residual Networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:33.398188Z

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Observation 8b809bfe-2852-4cc0-a0db-c093aaef7998 · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Aggregated residual transformations for deep neural networks,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.904755Z

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Observation 49b1c335-58e1-4803-8042-9712cf2c58ba · outbound

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

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:33.580483Z

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Observation ee3bfa16-3ff2-4233-aee8-5ab9e2e5d715 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 71

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Observation 9def4832-92c6-42ed-8374-ce9ad4723956 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Shufflenet v2: Practical guidelines for efficient cnn architecture design,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:33.750896Z

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Observation 8c9a6a4e-a479-46c5-afd8-2abb360709ab · outbound

This paper cites Shufflenet: An extremely effi- cient convolutional neural network for mobile devices,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Shufflenet: An extremely effi- cient convolutional neural network for mobile devices,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:33.832102Z

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Observation 7f2c10d1-8fe6-4c72-b363-aa18de1a0e59 · outbound

This paper cites Correlation congruence for knowledge distillation,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Correlation congruence for knowledge distillation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.703910Z

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Observation 367fc89f-1f0d-4cec-91a1-c04a8ec7e1a2 · outbound

This paper cites Knowledge distillation using hierarchical self-supervision augmented distribution,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Knowledge distillation using hierarchical self-supervision augmented distribution,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.576940Z

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Observation 3ea663be-d2cb-49c6-9666-f85dd17101f4 · outbound

This paper cites Multi-mode online knowledge distillation for self-supervised visual representation learning,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Multi-mode online knowledge distillation for self-supervised visual representation learning,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.389814Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4d388e3-cfed-4ab0-a7ee-6704417d0d85 · outbound

This paper cites Cross-layer distillation with semantic calibration,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Cross-layer distillation with semantic calibration,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.207403Z

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Observation d207bc0c-2190-453b-be24-ef6fb60caaf9 · outbound

This paper cites Rethinking the inception architecture for computer vision,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Rethinking the inception architecture for computer vision,

Reference 78

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Observation 74574223-7d38-4022-b726-6e41215ba8f1 · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:35.041836Z

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