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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3100 of 4240 papers

TitleStatusHype
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration—0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis—0
Data-Free Knowledge Transfer: A Survey—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
Data Techniques For Online End-to-end Speech Recognition—0
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models—0
DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images—0
DDK: Distilling Domain Knowledge for Efficient Large Language Models—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
Dealing with training and test segmentation mismatch: FBK@IWSLT2021—0
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers—0
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement—0
Debiased Distillation by Transplanting the Last Layer—0
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation—0
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space—0
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner—0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
Decoupled Alignment for Robust Plug-and-Play Adaptation—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation—0
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment—0
Deep Collective Knowledge Distillation—0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19—0
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback—0
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study—0
Deep Net Triage: Analyzing the Importance of Network Layers via Structural Compression—0
Deep Neural Compression Via Concurrent Pruning and Self-Distillation—0
Deep Neural Network Models Compression—0
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review—0
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection—0
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection—0
Deep-to-bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
Delving Deep into Semantic Relation Distillation—0
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training—0
Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillation—0
Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach—0
Designing Parameter and Compute Efficient Diffusion Transformers using Distillation—0
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism—0
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families—0
Show:102550
← PrevPage 62 of 85Next →
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