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 3251–3300 of 4240 papers

TitleStatusHype
Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models—0
Dynamic Textual Prompt For Rehearsal-free Lifelong Person Re-identification—0
Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks—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
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
ECG-guided individual identification via PPG—0
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models—0
EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis—0
EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation—0
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs—0
EdgeFusion: On-Device Text-to-Image Generation—0
EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation—0
Education distillation:getting student models to learn in shcools—0
EduPal leaves no professor behind: Supporting faculty via a peer-powered recommender system—0
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures—0
EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysis—0
Effective Decision Boundary Learning for Class Incremental Learning—0
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation—0
Effectiveness of Function Matching in Driving Scene Recognition—0
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching—0
Efficient Compression of Multitask Multilingual Speech Models—0
Efficient Controllable Multi-Task Architectures—0
Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation—0
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation—0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation—0
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Efficient Image Compression Using Advanced State Space Models—0
Efficient Inference via Universal LSH Kernel—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights—0
Efficient Knowledge Distillation of SAM for Medical Image Segmentation—0
Efficient Knowledge Distillation via Curriculum Extraction—0
Efficient Machine Translation with Model Pruning and Quantization—0
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation—0
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation—0
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning—0
Efficient Transformer Knowledge Distillation: A Performance Review—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