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 3001–3050 of 4240 papers

TitleStatusHype
Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing—0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer—0
Unraveling Key Factors of Knowledge Distillation—0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving—0
Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation—0
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model—0
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)—0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions—0
Unveiling Context-Aware Criteria in Self-Assessing LLMs—0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion—0
Using Advanced LLMs to Enhance Smaller LLMs: An Interpretable Knowledge Distillation Approach—0
Using a GAN to Generate Adversarial Examples to Facial Image Recognition—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Using Knowledge Distillation to improve interpretable models in a retail banking context—0
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation—0
Using the Past Knowledge to Improve Sentiment Classification—0
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models—0
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training—0
Variational Information Distillation for Knowledge Transfer—0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays—0
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework—0
VEM^2L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion—0
Vernacular? I Barely Know Her: Challenges with Style Control and Stereotyping—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning—0
Vi-LAD: Vision-Language Attention Distillation for Socially-Aware Robot Navigation in Dynamic Environments—0
Vision-Based Detection of Uncooperative Targets and Components on Small Satellites—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Visualizing the embedding space to explain the effect of knowledge distillation—0
Visualizing the Emergence of Intermediate Visual Patterns in DNNs—0
Visual-Language Model Knowledge Distillation Method for Image Quality Assessment—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation—0
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation—0
ViTKD: Practical Guidelines for ViT feature knowledge distillation—0
VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
VLM-KD: Knowledge Distillation from VLM for Long-Tail Visual Recognition—0
VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D Microscopic Cerebrovascular Segmentation—0
Wakening Past Concepts without Past Data: Class-incremental Learning from Placebos—0
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos—0
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications—0
Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion—0
Wasserstein Contrastive Representation Distillation—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