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 2151–2200 of 4240 papers

TitleStatusHype
Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation—0
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
Group Multi-View Transformer for 3D Shape Analysis with Spatial EncodingCode0
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual LearningCode0
X Modality Assisting RGBT Object Tracking—0
Cloud-Device Collaborative Learning for Multimodal Large Language Models—0
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation—0
Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessments—0
Revisiting Knowledge Distillation under Distribution ShiftCode0
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold—0
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark—0
Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental LearningCode0
StableKD: Breaking Inter-block Optimization Entanglement for Stable Knowledge DistillationCode0
DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization—0
Object Attribute Matters in Visual Question AnsweringCode0
Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders—0
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation—0
Decoupled Knowledge with Ensemble Learning for Online DistillationCode0
Mixed Distillation Helps Smaller Language Model Better Reasoning—0
Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image RetrievalCode0
Student as an Inherent Denoiser of Noisy Teacher—0
WAVER: Writing-style Agnostic Text-Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary KnowledgeCode0
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline—0
RdimKD: Generic Distillation Paradigm by Dimensionality Reduction—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial ProblemsCode0
Unraveling Key Factors of Knowledge Distillation—0
Mutual-Learning Knowledge Distillation for Nighttime UAV TrackingCode0
Cooperative Learning for Cost-Adaptive Inference—0
Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models—0
A dynamic interactive learning framework for automated 3D medical image segmentation—0
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation—0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation—0
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment—0
Improving Adversarial Robust Fairness via Anti-Bias Soft Label DistillationCode0
Understanding the Effect of Model Compression on Social Bias in Large Language ModelsCode0
Localized Symbolic Knowledge Distillation for Visual Commonsense ModelsCode0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidanceCode0
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video PairsCode0
Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation—0
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology—0
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation—0
S2P3: Self-Supervised Polarimetric Pose Prediction—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
Compression of end-to-end non-autoregressive image-to-speech system for low-resourced devices—0
Show:102550
← PrevPage 44 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