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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 3901–3950 of 4240 papers

TitleStatusHype
Data-Efficient Ranking Distillation for Image Retrieval—0
Knowledge Distillation Beyond Model Compression—0
Interactive Knowledge Distillation—0
SimulSpeech: End-to-End Simultaneous Speech to Text Translation—0
Improving Autoregressive NMT with Non-Autoregressive Model—0
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task—0
Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification with DocBERT—0
CASIA's System for IWSLT 2020 Open Domain Translation—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
On the Demystification of Knowledge Distillation: A Residual Network Perspective—0
Interpreting and Disentangling Feature Components of Various Complexity from DNNsCode0
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation—0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
Streaming Transformer ASR with Blockwise Synchronous Inference—0
Distilling Object Detectors with Task Adaptive Regularization—0
Prior knowledge distillation based on financial time series—0
Multi-fidelity Neural Architecture Search with Knowledge DistillationCode0
Pixel Invisibility: Detecting Objects Invisible in Color Images—0
Ensemble Distillation for Robust Model Fusion in Federated LearningCode0
Knowledge Distillation: A Survey—0
Continual Representation Learning for Biometric IdentificationCode0
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to EvolvabilityCode0
ResKD: Residual-Guided Knowledge Distillation—0
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression—0
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation—0
An Overview of Neural Network Compression—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Distilling Image Dehazing With Heterogeneous Task ImitationCode0
Apprentissage automatique de repr\'esentation de voix \`a l'aide d'une distillation de la connaissance pour le casting vocal (Learning voice representation using knowledge distillation for automatic voice casting )—0
ADINet: Attribute Driven Incremental Network for Retinal Image Classification—0
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Sub-Band Knowledge Distillation Framework for Speech Enhancement—0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders—0
Why distillation helps: a statistical perspective—0
Learning from a Lightweight Teacher for Efficient Knowledge Distillation—0
Joint Progressive Knowledge Distillation and Unsupervised Domain AdaptationCode0
Incremental Learning for End-to-End Automatic Speech Recognition—0
Distilling Knowledge from Pre-trained Language Models via Text Smoothing—0
Improving Non-autoregressive Neural Machine Translation with Monolingual Data—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference—0
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning—0
A Tailored Pre-Training Model for Task-Oriented Dialog GenerationCode0
A Study of Non-autoregressive Model for Sequence Generation—0
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation—0
Knowledge Distillation for Action Anticipation via Label Smoothing—0
Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation—0
Towards Robust Classification with Image Quality Assessment—0
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding—0
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models—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