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 3851–3900 of 4240 papers

TitleStatusHype
Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition—0
Autoregressive Knowledge Distillation through Imitation LearningCode0
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning—0
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification—0
BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks—0
Extending Label Smoothing Regularization with Self-Knowledge Distillation—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
Lifelong Object Detection—0
SAIL: Self-Augmented Graph Contrastive Learning—0
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge DistillationCode0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Initial Classifier Weights Replay for Memoryless Class Incremental Learning—0
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation—0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition—0
Active Class Incremental Learning for Imbalanced Datasets—0
Learn to Talk via Proactive Knowledge Transfer—0
Multi-Person Full Body Pose Estimation—0
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning—0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach—0
Cascaded channel pruning using hierarchical self-distillation—0
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition—0
Compression of Deep Learning Models for Text: A Survey—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Compact Speaker Embedding: lrx-vector—0
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification—0
Knowledge Distillation and Data Selection for Semi-Supervised Learning in CTC Acoustic Models—0
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback—0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition—0
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models—0
Prime-Aware Adaptive Distillation—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Feature Normalized Knowledge Distillation for Image ClassificationCode0
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -—0
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition—0
Local Correlation Consistency for Knowledge Distillation—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Multi-label Contrastive Predictive Coding—0
Interpretable Foreground Object Search As Knowledge Distillation—0
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis—0
Knowledge Distillation in Deep Learning and its Applications—0
UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled DataCode0
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection—0
Add a SideNet to your MainNet—0
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation—0
Representation Transfer by Optimal Transport—0
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer—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