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 1026–1050 of 4240 papers

TitleStatusHype
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT—0
DiagrammaticLearning: A Graphical Language for Compositional Training Regimes—0
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
An Efficient Active Learning Pipeline for Legal Text Classification—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—0
DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection—0
An Effective Deep Network for Head Pose Estimation without Keypoints—0
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices—0
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models—0
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families—0
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism—0
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation—0
Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation—0
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models—0
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing—0
EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation—0
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation—0
Designing Parameter and Compute Efficient Diffusion Transformers using Distillation—0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach—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