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 2651–2700 of 4240 papers

TitleStatusHype
A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning—0
A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation—0
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation—0
AIDE: Agentically Improve Visual Language Model with Domain Experts—0
AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation—0
AirNet: Neural Network Transmission over the Air—0
A Joint Sequential and Relational Model for Frame-Semantic Parsing—0
AKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks—0
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image—0
A Light-weight Deep Learning Model for Remote Sensing Image Classification—0
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition—0
A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate—0
AlignCap: Aligning Speech Emotion Captioning to Human Preferences—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
Aligning Teacher with Student Preferences for Tailored Training Data Generation—0
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition—0
Alleviating Catastrophic Forgetting of Incremental Object Detection via Within-Class and Between-Class Knowledge Distillation—0
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search—0
All You Need in Knowledge Distillation Is a Tailored Coordinate System—0
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
AMD: Adaptive Masked Distillation for Object Detection—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model—0
A metric learning approach for endoscopic kidney stone identification—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
Amortized Noisy Channel Neural Machine Translation—0
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
Analyzing Compression Techniques for Computer Vision—0
Analyzing Knowledge Distillation in Neural Machine Translation—0
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation—0
An Effective Deep Network for Head Pose Estimation without Keypoints—0
An Efficient Active Learning Pipeline for Legal Text Classification—0
An Efficient Detection and Control System for Underwater Docking using Machine Learning and Realistic Simulation: A Comprehensive Approach—0
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification—0
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation—0
An Efficient Private GPT Never Autoregressively Decodes—0
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation—0
An Empirical Investigation into the Effect of Parameter Choices in Knowledge Distillation—0
An Empirical Study of Efficient ASR Rescoring with Transformers—0
An Empirical Study of Leveraging Knowledge Distillation for Compressing Multilingual Neural Machine Translation Models—0
An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking—0
An Enhanced Low-Resolution Image Recognition Method for Traffic Environments—0
An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition—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