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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2601–2650 of 4240 papers

TitleStatusHype
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation—0
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings—0
Adaptive Beam Search to Enhance On-device Abstractive Summarization—0
Adaptive Deep Iris Feature Extractor at Arbitrary Resolutions—0
Adaptive Explicit Knowledge Transfer for Knowledge Distillation—0
Adaptive Group Robust Ensemble Knowledge Distillation—0
Adaptive Instance Distillation for Object Detection in Autonomous Driving—0
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models—0
Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers—0
Adaptive Label Smoothing with Self-Knowledge—0
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation—0
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning—0
Adaptive Multiplane Image Generation from a Single Internet Picture—0
Adaptive Regularization of Labels—0
Add a SideNet to your MainNet—0
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning—0
A Deep Hierarchical Feature Sparse Framework for Occluded Person Re-Identification—0
A deep Natural Language Inference predictor without language-specific training data—0
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images—0
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning—0
ADINet: Attribute Driven Incremental Network for Retinal Image Classification—0
A distillation based approach for the diagnosis of diseases—0
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
ADU: Adaptive Detection of Unknown Categories in Black-Box Domain Adaptation—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI—0
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity—0
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement—0
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks—0
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning—0
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
Adversarial Prompt Distillation for Vision-Language Models—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Adversarial Self-Supervised Data-Free Distillation for Text Classification—0
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples—0
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation—0
A dynamic interactive learning framework for automated 3D medical image segmentation—0
A Flexible Multi-Task Model for BERT Serving—0
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
After-Stroke Arm Paresis Detection using Kinematic Data—0
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone—0
Generalized Supervised Contrastive Learning—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy—0
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes—0
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation—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