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 2801–2850 of 4240 papers

TitleStatusHype
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
Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners—0
Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021—0
Technical report on Conversational Question Answering—0
Temporal Knowledge Distillation for On-device Audio Classification—0
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications—0
Temporal reasoning for timeline summarisation in social media—0
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks—0
TenTrans Large-Scale Multilingual Machine Translation System for WMT21—0
TernaryLLM: Ternarized Large Language Model—0
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation—0
Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation—0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
The economic trade-offs of large language models: A case study—0
The Estimation of Continual Causal Effect for Dataset Shifting Streams—0
The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation—0
The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task—0
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding—0
The Mininglamp Machine Translation System for WMT21—0
The NiuTrans Machine Translation Systems for WMT19—0
The NiuTrans Machine Translation Systems for WMT21—0
The NiuTrans Machine Translation Systems for WMT20—0
The NiuTrans System for the WMT 2021 Efficiency Task—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems—0
The Privileged Students: On the Value of Initialization in Multilingual Knowledge Distillation—0
The RoyalFlush System for the WMT 2022 Efficiency Task—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