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 3401–3450 of 4240 papers

TitleStatusHype
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding—0
A Short Study on Compressing Decoder-Based Language Models—0
Know your tools well: Better and faster QA with synthetic examples—0
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm—0
From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation—0
Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?—0
Kronecker Decomposition for GPT Compression—0
Language Modelling via Learning to Rank—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
CONetV2: Efficient Auto-Channel Size Optimization for CNNsCode0
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices—0
Rectifying the Data Bias in Knowledge Distillation—0
Towards Streaming Egocentric Action Anticipation—0
Towards Data-Free Domain GeneralizationCode0
Visualizing the embedding space to explain the effect of knowledge distillation—0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models—0
Peer Collaborative Learning for Polyphonic Sound Event Detection—0
Online Hyperparameter Meta-Learning with Hypergradient Distillation—0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis—0
Student Helping Teacher: Teacher Evolution via Self-Knowledge DistillationCode0
Deep Neural Compression Via Concurrent Pruning and Self-Distillation—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
Automated Channel Pruning with Learned Importance—0
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
SeqPATE: Differentially Private Text Generation via Knowledge Distillation—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
Scaling Fair Learning to Hundreds of Intersectional Groups—0
Self-Slimming Vision Transformer—0
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition—0
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
Adaptive Label Smoothing with Self-Knowledge—0
Representation Consolidation from Multiple Expert Teachers—0
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices—0
Wakening Past Concepts without Past Data: Class-incremental Learning from Placebos—0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Self-supervised Models are Good Teaching Assistants for Vision Transformers—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
Fast and Efficient Once-For-All Networks for Diverse Hardware Deployment—0
Self-Distilled Pruning Of Neural Networks—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
A Comprehensive Overhaul of Distilling Unconditional GANs—0
Reducing the Teacher-Student Gap via Adaptive Temperatures—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