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 1201–1250 of 4240 papers

TitleStatusHype
LookALike: Human Mimicry based collaborative decision making—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—0
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide ImagesCode1
Recurrent Drafter for Fast Speculative Decoding in Large Language ModelsCode3
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials—0
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine TranslationCode0
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike StreamsCode1
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object DetectionCode2
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task LearningCode0
CoroNetGAN: Controlled Pruning of GANs via Hypernetworks—0
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving—0
eDifFIQA: Towards Efficient Face Image Quality Assessment Based On Denoising Diffusion Probabilistic ModelsCode1
Low-Energy On-Device Personalization for MCUsCode0
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network StructureCode1
Distilling the Knowledge in Data Pruning—0
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-TuningCode2
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge DistillationCode0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Enhanced Sparsification via Stimulative Training—0
Answering Diverse Questions via Text Attached with Key Audio-Visual CluesCode0
Attention is all you need for boosting graph convolutional neural network—0
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic HashingCode1
Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees—0
V_kD: Improving Knowledge Distillation using Orthogonal ProjectionsCode2
Cooperative Classification and Rationalization for Graph GeneralizationCode0
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid InferenceCode0
Frequency Attention for Knowledge DistillationCode1
Scene Graph Aided Radiology Report Generation—0
Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation—0
Attention-guided Feature Distillation for Semantic Segmentation—0
Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples—0
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR FeaturesCode1
Self-Adapting Large Visual-Language Models to Edge Devices across Visual ModalitiesCode1
Privacy-preserving Fine-tuning of Large Language Models through Flatness—0
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech RecognitionCode0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Can Small Language Models be Good Reasoners for Sequential Recommendation?—0
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-DistillationCode0
Learning to Maximize Mutual Information for Chain-of-Thought DistillationCode0
PromptKD: Unsupervised Prompt Distillation for Vision-Language ModelsCode3
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—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