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 1901–1950 of 4240 papers

TitleStatusHype
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications—0
Error Exponent in Agnostic PAC Learning—0
Why does Knowledge Distillation Work? Rethink its Attention and Fidelity MechanismCode0
Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget—0
Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies—0
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation—0
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation—0
Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation—0
Compressed Meta-Optical Encoder for Image Classification—0
From LLM to NMT: Advancing Low-Resource Machine Translation with Claude—0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical ImagesCode0
CKD: Contrastive Knowledge Distillation from A Sample-wise PerspectiveCode0
Distributed Learning for Wi-Fi AP Load Prediction—0
Towards Multi-Morphology Controllers with Diversity and Knowledge DistillationCode0
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder—0
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities—0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation—0
Data-free Knowledge Distillation for Fine-grained Visual CategorizationCode0
EdgeFusion: On-Device Text-to-Image Generation—0
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning—0
GhostNetV3: Exploring the Training Strategies for Compact Models—0
LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge DistillationCode0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
ReffAKD: Resource-efficient Autoencoder-based Knowledge DistillationCode0
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision TransformersCode0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation—0
Remembering Transformer for Continual Learning—0
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
A predictive machine learning force field framework for liquid electrolyte development—0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
Robust feature knowledge distillation for enhanced performance of lightweight crack segmentation models—0
GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models—0
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model—0
Goldfish: An Efficient Federated Unlearning FrameworkCode0
On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small ModelsCode0
Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge DistillationCode0
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual ExplanationsCode0
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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