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Knowledge Distillation

Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.

Papers

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Showing 4101–4150 of 4240 papers

TitleStatusHype
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design—0
MiniVLN: Efficient Vision-and-Language Navigation by Progressive Knowledge Distillation—0
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning—0
Mitigating Cross-client GANs-based Attack in Federated Learning—0
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal—0
Mitigating Hallucination with ZeroG: An Advanced Knowledge Management Engine—0
Mixed Distillation Helps Smaller Language Model Better Reasoning—0
Mixed-Type Wafer Classification For Low Memory Devices Using Knowledge Distillation—0
MixKD: Towards Efficient Distillation of Large-scale Language Models—0
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation—0
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding—0
Modality-specific Distillation—0
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation—0
Model Compression and Efficient Inference for Large Language Models: A Survey—0
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography—0
Model Compression for Resource-Constrained Mobile Robots—0
Model Compression Methods for YOLOv5: A Review—0
Model compression using knowledge distillation with integrated gradients—0
Model Compression Using Optimal Transport—0
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System—0
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System—0
Model Distillation for Faithful Explanations of Medical Code Predictions—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation—0
Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Model Stitching by Functional Latent Alignment—0
Modifying Final Splits of Classification Tree for Fine-tuning Subpopulation Target in Policy Making—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
MoKD: Multi-Task Optimization for Knowledge Distillation—0
MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation—0
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data—0
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation—0
MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders—0
MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets—0
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All datasetsImageNetCIFAR-100COCO (Common Objects in Context)COCO 2017 valPASCAL VOCKITTI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43—Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53—Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93—Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8—Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6—Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9—Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7—Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55—Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5—Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86—Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76—Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6—Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28—Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08—Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93—Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68—Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5—Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68—Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP77.16—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP73.73—Unverified
3ADLIK-Faster (T: Faster R-CNN vit-base S: Faster R-CNN deit-small) box AP47.6—Unverified
4ADLIK-Mask (T: Mask R-CNN vit-base S: Mask R-CNN deit-small)mask AP42.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet50))AP@0.561.8—Unverified
2ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet18))AP@0.557.96—Unverified
3ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(mobilenet-v2))AP@0.555.18—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14—Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43—Unverified