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 2751–2800 of 4240 papers

TitleStatusHype
Stealing Neural Networks via Timing Side Channels—0
Step Out and Seek Around: On Warm-Start Training with Incremental Data—0
Stereo-Knowledge Distillation from dpMV to Dual Pixels for Light Field Video Reconstruction—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
STEVE Series: Step-by-Step Construction of Agent Systems in Minecraft—0
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
Strategic Fusion Optimizes Transformer Compression—0
Streaming egocentric action anticipation: An evaluation scheme and approach—0
Streaming Transformer ASR with Blockwise Synchronous Inference—0
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation—0
Structural Knowledge Distillation for Object Detection—0
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization—0
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection—0
Structured Pruning of Neural Networks with Budget-Aware Regularization—0
StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition—0
Student as an Inherent Denoiser of Noisy Teacher—0
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher—0
Student-friendly Knowledge Distillation—0
Student Network Learning via Evolutionary Knowledge Distillation—0
Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation—0
Students Parrot Their Teachers: Membership Inference on Model Distillation—0
Students taught by multimodal teachers are superior action recognizers—0
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification—0
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation—0
Style over Substance: Distilled Language Models Reason Via Stylistic Replication—0
Sub-Band Knowledge Distillation Framework for Speech Enhancement—0
Subclass Knowledge Distillation with Known Subclass Labels—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
Supervised Graph Contrastive Pretraining for Text Classification—0
Supervision Complexity and its Role in Knowledge Distillation—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Knowledge Distillation in Federated Edge Learning: A Survey—0
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning—0
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders—0
Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks—0
Synthetic Unknown Class Learning for Learning Unknowns—0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation—0
Take a Prior from Other Tasks for Severe Blur Removal—0
TalkingMachines: Real-Time Audio-Driven FaceTime-Style Video via Autoregressive Diffusion Models—0
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication—0
Target-driven Self-Distillation for Partial Observed Trajectories Forecasting—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