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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3001–3050 of 4240 papers

TitleStatusHype
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
CORSD: Class-Oriented Relational Self Distillation—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers—0
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
CoupleFace: Relation Matters for Face Recognition Distillation—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation—0
Cross Architecture Distillation for Face Recognition—0
Cross-Architecture Knowledge Distillation—0
Cross-Class Feature Augmentation for Class Incremental Learning—0
Cross domain knowledge compression in realtime optical flow prediction on ultrasound sequences—0
Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages—0
Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages—0
Cross-modal Contrastive Distillation for Instructional Activity Anticipation—0
Cross Modal Distillation for Flood Extent Mapping—0
Cross-modal knowledge distillation for action recognition—0
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification—0
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
Crowd Counting with Online Knowledge Learning—0
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation—0
D^3ETR: Decoder Distillation for Detection Transformer—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
DaFKD: Domain-Aware Federated Knowledge Distillation—0
DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
Data-Driven Compression of Convolutional Neural Networks—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Data-Efficient Ranking Distillation for Image Retrieval—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Show:102550
← PrevPage 61 of 85Next →
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