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 1601–1650 of 4240 papers

TitleStatusHype
Cross-Architecture Knowledge Distillation—0
Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation—0
A Technical Study into Small Reasoning Language Models—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold—0
Compressing GANs using Knowledge Distillation—0
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
FedKD: Communication Efficient Federated Learning via Knowledge Distillation—0
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone—0
Handling Long-tailed Feature Distribution in AdderNets—0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation—0
Compressing Deep Image Super-resolution Models—0
FedRAD: Federated Robust Adaptive Distillation—0
Hands-on Guidance for Distilling Object Detectors—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
HARD: Hard Augmentations for Robust Distillation—0
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation—0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction—0
Enhanced Sparsification via Stimulative Training—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
FEED: Feature-level Ensemble for Knowledge Distillation—0
Compressed Meta-Optical Encoder for Image Classification—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
After-Stroke Arm Paresis Detection using Kinematic Data—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Cross Modal Distillation for Flood Extent Mapping—0
End-to-End Speech Translation with Knowledge Distillation—0
Few-shot learning of neural networks from scratch by pseudo example optimization—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022—0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
A methodology for training homomorphicencryption friendly neural networks—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
Fine-Grained Distillation for Long Document Retrieval—0
Fine-grained Image Retrieval via Dual-Vision Adaptation—0
Cross-modal knowledge distillation for action recognition—0
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—0
Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation—0
Show:102550
← PrevPage 33 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