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 26262650 of 4240 papers

TitleStatusHype
Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs0
SEDD-PCC: A Single Encoder-Dual Decoder Framework For End-To-End Learned Point Cloud Compression0
Segment Any RGB-Thermal Model with Language-aided Distillation0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models0
Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning0
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling0
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation0
Self-Cooperation Knowledge Distillation for Novel Class Discovery0
Self-Distillation Amplifies Regularization in Hilbert Space0
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks0
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation0
Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification0
Self-Distilled Pruning Of Neural Networks0
Self-Distilled Pruning of Neural Networks0
Self-Evolution Knowledge Distillation for LLM-based Machine Translation0
A New Training Framework for Deep Neural Network0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK0
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images0
Self-Knowledge Distillation for Learning Ambiguity0
Self-Knowledge Distillation for Surgical Phase Recognition0
Self-Knowledge Distillation in Natural Language Processing0
Self-Knowledge Distillation via Dropout0
Self-Referenced Deep Learning0
Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation0
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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