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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 3001–3050 of 4240 papers

TitleStatusHype
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Boosting Contrastive Learning with Relation Knowledge Distillation—0
Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge DistillationCode0
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial ImagesCode1
Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge DistillationCode1
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake ImagesCode0
Safe Distillation Box—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Extracting knowledge from features with multilevel abstraction—0
KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification—0
Tiny-NewsRec: Effective and Efficient PLM-based News RecommendationCode1
FedRAD: Federated Robust Adaptive Distillation—0
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
Information Theoretic Representation DistillationCode1
The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single ImageCode1
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social MediaCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped MatricesCode0
Handling Long-tailed Feature Distribution in AdderNets—0
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge DistillationCode1
Comprehensive Knowledge Distillation with Causal InterventionCode1
Analyzing the Confidentiality of Undistillable Teachers in Knowledge DistillationCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Using a GAN to Generate Adversarial Examples to Facial Image Recognition—0
Improved Knowledge Distillation via Adversarial Collaboration—0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
WiFi-based Multi-task SensingCode1
Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs—0
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge DistillationCode1
Self-slimmed Vision TransformerCode1
Domain-Agnostic Clustering with Self-Distillation—0
Semi-Online Knowledge DistillationCode0
Focal and Global Knowledge Distillation for DetectorsCode1
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling—0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion—0
Toxicity Detection can be Sensitive to the Conversational Context—0
Dynamically pruning segformer for efficient semantic segmentation—0
Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity RecognitionCode0
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation—0
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation—0
Multi-Granularity Contrastive Knowledge Distillation for Multimodal Named Entity Recognition—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
A Flexible Multi-Task Model for BERT Serving—0
Compositional Data Augmentation for Abstractive Conversation Summarization—0
Deep-to-bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation—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