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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3250 of 4240 papers

TitleStatusHype
Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling—0
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild—0
Domain-Agnostic Clustering with Self-Distillation—0
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Domain-invariant Feature Exploration for Domain Generalization—0
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection—0
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain—0
Domain-specific knowledge distillation yields smaller and better models for conversational commerce—0
Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation—0
Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting—0
Don't be picky, all students in the right family can learn from good teachers—0
Don't Throw Away Data: Better Sequence Knowledge Distillation—0
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency—0
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches—0
Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification—0
Double Similarity Distillation for Semantic Image Segmentation—0
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model—0
DreamTeacher: Pretraining Image Backbones with Deep Generative Models—0
DRKF: Distilled Rotated Kernel Fusion for Efficient Rotation Invariant Descriptors in Local Feature Matching—0
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network—0
DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization—0
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning—0
DST: Dynamic Substitute Training for Data-free Black-box Attack—0
DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis—0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
Dual Discriminator Adversarial Distillation for Data-free Model Compression—0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head—0
Dual Knowledge Distillation for Efficient Sound Event Detection—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation—0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
DualVC 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion—0
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding—0
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset—0
DVFL: A Vertical Federated Learning Method for Dynamic Data—0
DyLiN: Making Light Field Networks Dynamic—0
Dynamic Activation with Knowledge Distillation for Energy-Efficient Spiking NN Ensembles—0
Dynamically pruning segformer for efficient semantic segmentation—0
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing—0
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning—0
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection—0
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Show:102550
← PrevPage 65 of 85Next →
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