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 1151–1200 of 4240 papers

TitleStatusHype
Modality-Independent Brain Lesion Segmentation with Privacy-aware Continual LearningCode0
MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control—0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation—0
OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery—0
Efficient Knowledge Distillation via Curriculum Extraction—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Improving Acoustic Scene Classification with City Features—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
DCA: Dividing and Conquering Amnesia in Incremental Object DetectionCode0
Technical Report for the 5th CLVision Challenge at CVPR: Addressing the Class-Incremental with Repetition using Unlabeled Data -- 4th Place SolutionCode0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI—0
Distilling 3D distinctive local descriptors for 6D pose estimation—0
KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition—0
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly DetectionCode0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose EstimationCode0
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation—0
Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep LearningCode0
Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Adaptive Temperature Based on Logits Correlation in Knowledge DistillationCode0
Vi-LAD: Vision-Language Attention Distillation for Socially-Aware Robot Navigation in Dynamic Environments—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
xVLM2Vec: Adapting LVLM-based embedding models to multilinguality using Self-Knowledge Distillation—0
Training Domain Draft Models for Speculative Decoding: Best Practices and Insights—0
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting—0
PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning—0
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification—0
Small Vision-Language Models: A Survey on Compact Architectures and Techniques—0
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Improving SAM for Camouflaged Object Detection via Dual Stream Adapters—0
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation—0
No Forgetting Learning: Memory-free Continual Learning—0
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization—0
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks—0
Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge DistillationCode0
Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model—0
DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems—0
Mamba base PKD for efficient knowledge compression—0
VRM: Knowledge Distillation via Virtual Relation Matching—0
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs—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