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 1551–1600 of 4240 papers

TitleStatusHype
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation—0
Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity—0
Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Linear Projections of Teacher Embeddings for Few-Class Distillation—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
MiniVLN: Efficient Vision-and-Language Navigation by Progressive Knowledge Distillation—0
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge IntegrationCode0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion—0
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models—0
Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation—0
Kendall's τ Coefficient for Logits Distillation—0
Shape-intensity knowledge distillation for robust medical image segmentationCode0
Weak-to-Strong Backdoor Attack for Large Language Models—0
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling—0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events—0
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation—0
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization—0
Privacy Evaluation Benchmarks for NLP ModelsCode0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
DSG-KD: Knowledge Distillation from Domain-Specific to General Language ModelsCode0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
Prior Knowledge Distillation Network for Face Super-Resolution—0
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models—0
On Importance of Pruning and Distillation for Efficient Low Resource NLP—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Simple Unsupervised Knowledge Distillation With Space Similarity—0
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural NetworksCode0
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward—0
Bayesian-Optimized One-Step Diffusion Model with Knowledge Distillation for Real-Time 3D Human Motion Prediction—0
Small Language Models are Equation Reasoners—0
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models—0
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT DistillationCode0
Enhancing SLM via ChatGPT and Dataset Augmentation—0
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution AlignmentCode0
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights—0
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
RUIE: Retrieval-based Unified Information Extraction using Large Language ModelCode0
EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysis—0
Applications of Knowledge Distillation in Remote Sensing: A Survey—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