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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 1501–1550 of 4240 papers

TitleStatusHype
Improving Pronunciation and Accent Conversion through Knowledge Distillation And Synthetic Ground-Truth from Native TTS—0
DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational SearchCode0
Interpreting Microbiome Relative Abundance Data Using Symbolic RegressionCode0
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
Preview-based Category Contrastive Learning for Knowledge Distillation—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach—0
FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs—0
CAKD: A Correlation-Aware Knowledge Distillation Framework Based on Decoupling Kullback-Leibler Divergence—0
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm—0
TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant—0
SAM-Guided Masked Token Prediction for 3D Scene Understanding—0
Proactive Detection and Calibration of Seasonal Advertisements with Multimodal Large Language Models—0
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL—0
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
Temperature-Centric Investigation of Speculative Decoding with Knowledge DistillationCode0
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI SegmentationCode0
ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object DetectionCode0
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition—0
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering DatasetsCode0
Distilling Invariant Representations with Dual Augmentation—0
Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both—0
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
A Lightweight Target-Driven Network of Stereo Matching for Inland WaterwaysCode0
Relational Diffusion Distillation for Efficient Image GenerationCode0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
SNN-PAR: Energy Efficient Pedestrian Attribute Recognition via Spiking Neural Networks—0
Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing—0
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching—0
S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from ServerCode0
ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillation—0
Progressive distillation induces an implicit curriculum—0
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMsCode0
CAPEEN: Image Captioning with Early Exits and Knowledge DistillationCode0
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech—0
Accelerating Diffusion Models with One-to-Many Knowledge Distillation—0
Gap Preserving Distillation by Building Bidirectional Mappings with A Dynamic Teacher—0
Self-Supervised Keypoint Detection with Distilled Depth Keypoint Representation—0
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models—0
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep NetworksCode0
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge DistillationCode0
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and TasksCode0
PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation—0
"No Matter What You Do": Purifying GNN Models via Backdoor UnlearningCode0
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code GenerationCode0
Self-Updatable Large Language Models with Parameter Integration—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