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 1251–1300 of 4240 papers

TitleStatusHype
Gemma 2: Improving Open Language Models at a Practical Size—0
A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction—0
Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation—0
Cross domain knowledge compression in realtime optical flow prediction on ultrasound sequences—0
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings—0
Cross-Class Feature Augmentation for Class Incremental Learning—0
Cross-Architecture Knowledge Distillation—0
Cross Architecture Distillation for Face Recognition—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition—0
Exploring Extreme Quantization in Spiking Language Models—0
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
AWF: Adaptive Weight Fusion for Enhanced Class Incremental Semantic Segmentation—0
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices—0
A Light-weight Deep Learning Model for Remote Sensing Image Classification—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis—0
Aware of the History: Trajectory Forecasting with the Local Behavior Data—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
CoupleFace: Relation Matters for Face Recognition Distillation—0
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image—0
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting—0
A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models—0
1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022—0
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
AUTOSUMM: Automatic Model Creation for Text Summarization—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials—0
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
CORSD: Class-Oriented Relational Self Distillation—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
Automatic Mixed-Precision Quantization Search of BERT—0
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible—0
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR—0
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models—0
CoroNetGAN: Controlled Pruning of GANs via Hypernetworks—0
ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Adapting Models to Signal Degradation using Distillation—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—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