SOTAVerified

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 1051–1075 of 4240 papers

TitleStatusHype
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—0
HoverFast: an accurate, high-throughput, clinically deployable nuclear segmentation tool for brightfield digital pathology images—0
Why Not Transform Chat Large Language Models to Non-English?Code0
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch—0
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian DetectionCode1
Active Object Detection with Knowledge Aggregation and Distillation from Large ModelsCode0
CLRKDNet: Speeding up Lane Detection with Knowledge DistillationCode1
TinyM^2Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices—0
Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models—0
Stereo-Knowledge Distillation from dpMV to Dual Pixels for Light Field Video Reconstruction—0
Hierarchical Selective Classification—0
Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation—0
Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation—0
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic AnchorsCode1
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue GenerationCode0
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models—0
GLiRA: Black-Box Membership Inference Attack via Knowledge DistillationCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—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