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 3451–3500 of 4240 papers

TitleStatusHype
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Feature Kernel Distillation—0
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization—0
Prototypical Contrastive Predictive Coding—0
Improving Question Answering Performance Using Knowledge Distillation and Active LearningCode0
Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better—0
Recent Advances of Continual Learning in Computer Vision: An Overview—0
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge DistillationCode0
The NiuTrans Machine Translation Systems for WMT21—0
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering—0
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network—0
Knowledge Distillation with Noisy Labels for Natural Language Understanding—0
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation—0
Releasing Graph Neural Networks with Differential Privacy GuaranteesCode0
Towards Full Utilization on Mask Task for Distilling PLMs into NMT—0
Label Assignment Distillation for Object Detection—0
New Perspective on Progressive GANs Distillation for One-class Novelty Detection—0
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
A Note on Knowledge Distillation Loss Function for Object Classification—0
Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs—0
UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation—0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
On the Efficiency of Subclass Knowledge Distillation in Classification Tasks—0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing—0
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation—0
Learning to Teach with Student Feedback—0
Dual Correction Strategy for Ranking Distillation in Top-N Recommender SystemCode0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-SupervisionCode0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
Knowledge Distillation with BERT for Image Tag-Based Privacy Prediction—0
FedKD: Communication Efficient Federated Learning via Knowledge Distillation—0
Lipschitz Continuity Guided Knowledge Distillation—0
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain ConversationCode0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation—0
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
Personalised Federated Learning: A Combinational Approach—0
Boosting of Head Pose Estimation by Knowledge Distillation—0
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
Online Continual Learning For Visual Food Classification—0
Multi-granularity for knowledge distillationCode0
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data—0
Lifelong Intent Detection via Multi-Strategy Rebalancing—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