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 701–725 of 4240 papers

TitleStatusHype
DSG-KD: Knowledge Distillation from Domain-Specific to General Language ModelsCode0
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
Prior Knowledge Distillation Network for Face Super-Resolution—0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
On Importance of Pruning and Distillation for Efficient Low Resource NLP—0
Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning TasksCode1
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Simple Unsupervised Knowledge Distillation With Space Similarity—0
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT DistillationCode0
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings—0
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution AlignmentCode0
Enhancing SLM via ChatGPT and Dataset Augmentation—0
Bayesian-Optimized One-Step Diffusion Model with Knowledge Distillation for Real-Time 3D Human Motion Prediction—0
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights—0
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models—0
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward—0
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural NetworksCode0
Small Language Models are Equation Reasoners—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysis—0
RUIE: Retrieval-based Unified Information Extraction using Large Language ModelCode0
Applications of Knowledge Distillation in Remote Sensing: A Survey—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