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 1451–1500 of 4240 papers

TitleStatusHype
InDistill: Information flow-preserving knowledge distillation for model compressionCode0
Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge DistillationCode0
Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose ForecastingCode0
Hybrid Data-Free Knowledge DistillationCode0
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic SegmentationCode0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity MatchingCode0
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View DistillationCode0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
DOGe: Defensive Output Generation for LLM Protection Against Knowledge DistillationCode0
Context Unaware Knowledge Distillation for Image RetrievalCode0
Does Training with Synthetic Data Truly Protect Privacy?Code0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog SystemsCode0
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning StrategyCode0
CKD: Contrastive Knowledge Distillation from A Sample-wise PerspectiveCode0
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object DetectionCode0
Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple LevelsCode0
Low-Cost Self-Ensembles Based on Multi-Branch Transformation and Grouped ConvolutionCode0
How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face RecognitionCode0
How to Train the Teacher Model for Effective Knowledge DistillationCode0
HiTSR: A Hierarchical Transformer for Reference-based Super-ResolutionCode0
Highlight Every Step: Knowledge Distillation via Collaborative TeachingCode0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical ChromoendoscopyCode0
Applying Knowledge Distillation to Improve Weed Mapping With DronesCode0
Chemical transformer compression for accelerating both training and inference of molecular modelingCode0
Distribution Aligned Semantics Adaption for Lifelong Person Re-IdentificationCode0
Distributed Soft Actor-Critic with Multivariate Reward Representation and Knowledge DistillationCode0
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware DetectorCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
Induced Model Matching: How Restricted Models Can Help Larger OnesCode0
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMaxCode0
Group Multi-View Transformer for 3D Shape Analysis with Spatial EncodingCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric LearningCode0
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language ProcessingCode0
Guiding Frame-Level CTC Alignments Using Self-knowledge DistillationCode0
Exploring Social Media for Early Detection of Depression in COVID-19 PatientsCode0
Exploring Target Representations for Masked AutoencodersCode0
Graph Knowledge Distillation to Mixture of ExpertsCode0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
Graph Entropy Minimization for Semi-supervised Node ClassificationCode0
Distill n' Explain: explaining graph neural networks using simple surrogatesCode0
GOTHAM: Graph Class Incremental Learning Framework under Weak SupervisionCode0
Distilling Virtual Examples for Long-tailed RecognitionCode0
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series DataCode0
Spending Your Winning Lottery Better After Drawing ItCode0
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity RecognitionCode0
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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