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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 1601–1650 of 4240 papers

TitleStatusHype
Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation ModelCode0
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference—0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Integrated Multi-Level Knowledge Distillation for Enhanced Speaker Verification—0
Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility—0
AWF: Adaptive Weight Fusion for Enhanced Class Incremental Semantic Segmentation—0
DiReDi: Distillation and Reverse Distillation for AIoT Applications—0
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios—0
Enhancing CTC-Based Visual Speech Recognition—0
DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
How Redundant Is the Transformer Stack in Speech Representation Models?—0
Knowledge Distillation via Query Selection for Detection Transformer—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Joint Input and Output Coordination for Class-Incremental Learning—0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
LoCa: Logit Calibration for Knowledge Distillation—0
SCARF: Scalable Continual Learning Framework for Memory-efficient Multiple Neural Radiance Fields—0
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration—0
Experimentation in Content Moderation using RWKV—0
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model—0
Non-target Divergence Hypothesis: Toward Understanding Domain Gaps in Cross-Modal Knowledge Distillation—0
Efficient Image Compression Using Advanced State Space Models—0
Collaborative Learning for Enhanced Unsupervised Domain Adaptation—0
Adaptive Explicit Knowledge Transfer for Knowledge Distillation—0
Improving Apple Object Detection with Occlusion-Enhanced Distillation—0
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment—0
HiTSR: A Hierarchical Transformer for Reference-based Super-ResolutionCode0
How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face RecognitionCode0
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation DetectionCode0
MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face RecognitionCode0
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling—0
VLM-KD: Knowledge Distillation from VLM for Long-Tail Visual Recognition—0
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation—0
Online pre-training with long-form videos—0
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation—0
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems—0
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection—0
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines—0
On-Device Language Models: A Comprehensive ReviewCode0
Bring the Power of Diffusion Model to Defect Detection—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