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 3801–3850 of 4240 papers

TitleStatusHype
ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Activation Map Adaptation for Effective Knowledge Distillation—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Two-stage Textual Knowledge Distillation for End-to-End Spoken Language UnderstandingCode0
Pre-trained Summarization DistillationCode0
Improved Synthetic Training for Reading Comprehension—0
Iterative Graph Self-Distillation—0
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation—0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering—0
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation—0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation—0
Noisy Neural Network Compression for Analog Storage Devices—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review MechanismCode0
AutoADR: Automatic Model Design for Ad Relevance—0
MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings—0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
Structural Knowledge Distillation: Tractably Distilling Information for Structured PredictorCode0
Adversarial Self-Supervised Data-Free Distillation for Text Classification—0
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System—0
Locally Linear Region Knowledge Distillation—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models—0
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling—0
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review—0
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate LayersCode0
A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions—0
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation—0
Neighbourhood Distillation: On the benefits of non end-to-end distillation—0
Online Knowledge Distillation via Multi-branch Diversity Enhancement—0
WeChat Neural Machine Translation Systems for WMT20—0
Improved Knowledge Distillation via Full Kernel Matrix TransferCode0
Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach—0
Kernel Based Progressive Distillation for Adder Neural Networks—0
Pea-KD: Parameter-efficient and accurate Knowledge Distillation—0
TernaryBERT: Distillation-aware Ultra-low Bit BERTCode0
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias—0
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks—0
Weight Distillation: Transferring the Knowledge in Neural Network Parameters—0
Introspective Learning by Distilling Knowledge from Online Self-explanation—0
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning—0
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP—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