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 2201–2250 of 4240 papers

TitleStatusHype
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
Targeted Forgetting of Image Subgroups in CLIP Models—0
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant—0
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation—0
Task-Balanced Distillation for Object Detection—0
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation—0
Task Integration Distillation for Object Detectors—0
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation—0
Teacher's pet: understanding and mitigating biases in distillation—0
Teacher-Student Architecture for Knowledge Learning: A Survey—0
Teacher-Student Architecture for Knowledge Distillation: A Survey—0
Teacher-Student chain for efficient semi-supervised histology image classification—0
Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators—0
Distilled Siamese Networks for Visual Tracking—0
Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion—0
Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion—0
Teacher-Student Training for Robust Tacotron-based TTS—0
Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios—0
"Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation—0
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
Teaching Small Language Models to Reason—0
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection—0
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings—0
Teach model to answer questions after comprehending the document—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