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 1701–1725 of 4240 papers

TitleStatusHype
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
GAN-Knowledge Distillation for one-stage Object Detection—0
Gap Preserving Distillation by Building Bidirectional Mappings with A Dynamic Teacher—0
GazeGen: Gaze-Driven User Interaction for Visual Content Generation—0
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning—0
Knowledge distillation for optimization of quantized deep neural networks—0
Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification—0
Generalized Continual Zero-Shot Learning—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation—0
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation—0
Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning—0
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval—0
Show:102550
← PrevPage 69 of 170Next →
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