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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 201–250 of 4240 papers

TitleStatusHype
OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-ReflectionCode1
Knowledge Distillation Approaches for Accurate and Efficient Recommender SystemCode1
Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object DetectionCode1
Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object DetectionCode1
Mitigating Background Shift in Class-Incremental Semantic SegmentationCode1
Discriminative and Consistent Representation DistillationCode1
Relational Representation DistillationCode1
LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object DetectionCode1
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge DistillationCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
AdaDistill: Adaptive Knowledge Distillation for Deep Face RecognitionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression RecognitionCode1
MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language NavigationCode1
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge GraphsCode1
BiLD: Bi-directional Logits Difference Loss for Large Language Model DistillationCode1
Lightweight Model Pre-training via Language Guided Knowledge DistillationCode1
Small Scale Data-Free Knowledge DistillationCode1
CTC-based Non-autoregressive Textless Speech-to-Speech TranslationCode1
DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuningCode1
LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face VerificationCode1
Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image SegmentationCode1
Continual Collaborative Distillation for Recommender SystemCode1
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
LoReTrack: Efficient and Accurate Low-Resolution Transformer TrackingCode1
Rethinking Early-Fusion Strategies for Improved Multispectral Object DetectionCode1
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis ModelsCode1
Recurrent Early Exits for Federated Learning with Heterogeneous ClientsCode1
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian DetectionCode1
CLRKDNet: Speeding up Lane Detection with Knowledge DistillationCode1
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic AnchorsCode1
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly DetectionCode1
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelCode1
CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge DistillationCode1
Retrieval-Oriented Knowledge for Click-Through Rate PredictionCode1
Dynamic Temperature Knowledge DistillationCode1
Camera clustering for scalable stream-based active distillationCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as TeachersCode1
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object DetectionCode1
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language ModelsCode1
Rethinking Pruning for Vision-Language Models: Strategies for Effective Sparsity and Performance RestorationCode1
TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge DistillationCode1
PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic SegmentationCode1
Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-DistillationCode1
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive LearningCode1
ToXCL: A Unified Framework for Toxic Speech Detection and ExplanationCode1
iDAT: inverse Distillation Adapter-TuningCode1
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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