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

Showing 37513775 of 4240 papers

TitleStatusHype
Initial Classifier Weights Replay for Memoryless Class Incremental Learning0
Unpaired Learning of Deep Image DenoisingCode1
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation0
Performance Optimization for Federated Person Re-identification via Benchmark AnalysisCode1
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition0
Active Class Incremental Learning for Imbalanced Datasets0
Multi-Person Full Body Pose Estimation0
Learn to Talk via Proactive Knowledge Transfer0
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach0
PARADE: Passage Representation Aggregation for Document RerankingCode1
Knowledge Transfer via Dense Cross-Layer Mutual-DistillationCode1
Cascaded channel pruning using hierarchical self-distillation0
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition0
Compression of Deep Learning Models for Text: A Survey0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer0
Compact Speaker Embedding: lrx-vector0
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification0
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback0
Knowledge Distillation and Data Selection for Semi-Supervised Learning in CTC Acoustic Models0
Distilling the Knowledge of BERT for Sequence-to-Sequence ASRCode1
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition0
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models0
Prime-Aware Adaptive Distillation0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43Unverified