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 2201–2250 of 4240 papers

TitleStatusHype
LayerCollapse: Adaptive compression of neural networks—0
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge DistillationCode0
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs—0
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor SegmentationCode0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate PredictionCode0
Wired Perspectives: Multi-View Wire Art Embraces Generative AI—0
Unlearning via Sparse Representations—0
Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Pseudo-label Correction for Instance-dependent Noise Using Teacher-student Framework—0
Maximizing Discrimination Capability of Knowledge Distillation with Energy Function—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Efficient and Robust Jet Tagging at the LHC with Knowledge DistillationCode0
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Education distillation:getting student models to learn in shcools—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers—0
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction—0
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series—0
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence GenerationCode0
Distilling the Unknown to Unveil CertaintyCode0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Batch Selection and Communication for Active Learning with Edge Labeling—0
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings—0
On Elastic Language Models—0
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency—0
Object-centric Cross-modal Feature Distillation for Event-based Object Detection—0
Text Representation Distillation via Information Bottleneck PrincipleCode0
Preference-Consistent Knowledge Distillation for Recommender SystemCode0
Bridging Dimensions: Confident Reachability for High-Dimensional ControllersCode0
Reducing Spatial Fitting Error in Distillation of Denoising Diffusion ModelsCode0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated dataCode0
What is Lost in Knowledge Distillation?—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
Comparative Knowledge DistillationCode0
After-Stroke Arm Paresis Detection using Kinematic Data—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
An Efficient Detection and Control System for Underwater Docking using Machine Learning and Realistic Simulation: A Comprehensive Approach—0
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition—0
Group Distributionally Robust Knowledge Distillation—0
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks—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