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 3701–3750 of 4240 papers

TitleStatusHype
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Bridging the gap between Human Action Recognition and Online Action Detection—0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19—0
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation—0
Incremental Knowledge Based Question Answering—0
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
Interpretable discovery of new semiconductors with machine learning—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
Spending Your Winning Lottery Better After Drawing ItCode0
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding—0
Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection—0
Knowledge distillation via softmax regression representation learning—0
Robust Overfitting may be mitigated by properly learned smoothening—0
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition—0
Understanding Adversarial Attacks on Autoencoders—0
Understanding Knowledge Distillation—0
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning—0
Disentanglement, Visualization and Analysis of Complex Features in DNNs—0
Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer—0
Explicit Connection Distillation—0
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective—0
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher—0
Learning from deep model via exploring local targets—0
Improving De-Raining Generalization via Neural Reorganization—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Contextual Knowledge Distillation for Transformer Compression—0
Don't be picky, all students in the right family can learn from good teachers—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Knowledge Distillation based Ensemble Learning for Neural Machine Translation—0
Distilling Global and Local Logits With Densely Connected RelationsCode0
Kernel Methods in Hyperbolic Spaces—0
Fully Synthetic Data Improves Neural Machine Translation with Knowledge Distillation—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays—0
Understanding and Improving Lexical Choice in Non-Autoregressive Translation—0
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation—0
Towards a Universal Continuous Knowledge Base—0
Future-Guided Incremental Transformer for Simultaneous Translation—0
AttentionLite: Towards Efficient Self-Attention Models for Vision—0
Diverse Knowledge Distillation for End-to-End Person Search—0
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—0
Wasserstein Contrastive Representation Distillation—0
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding—0
Periocular Embedding Learning with Consistent Knowledge Distillation from Face—0
Improving Task-Agnostic BERT Distillation with Layer Mapping Search—0
Reinforced Multi-Teacher Selection for 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