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 1201–1250 of 4240 papers

TitleStatusHype
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models—0
Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval—0
Granite Embedding Models—0
Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
From underwater to aerial: a novel multi-scale knowledge distillation approach for coral reef monitoringCode0
Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation—0
Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing—0
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers—0
A Transformer-in-Transformer Network Utilizing Knowledge Distillation for Image Recognition—0
CLIMB-3D: Continual Learning for Imbalanced 3D Instance SegmentationCode0
PQDAST: Depth-Aware Arbitrary Style Transfer for Games via Perceptual Quality-Guided Distillation—0
Knowledge Distillation with Training Wheels—0
EDocNet: Efficient Datasheet Layout Analysis Based on Focus and Global Knowledge Distillation—0
A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier ModelsCode0
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
Designing Parameter and Compute Efficient Diffusion Transformers using Distillation—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
Modifying Final Splits of Classification Tree for Fine-tuning Subpopulation Target in Policy Making—0
Dynamic Activation with Knowledge Distillation for Energy-Efficient Spiking NN Ensembles—0
Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture—0
Capturing Rich Behavior Representations: A Dynamic Action Semantic-Aware Graph Transformer for Video Captioning—0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Enhancing Semi-supervised Learning with Zero-shot Pseudolabels—0
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions—0
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models—0
Does Training with Synthetic Data Truly Protect Privacy?Code0
Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social RecommendationCode0
Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge DistillationCode0
Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs—0
CLoCKDistill: Consistent Location-and-Context-aware Knowledge Distillation for DETRs—0
AIDE: Agentically Improve Visual Language Model with Domain Experts—0
LLM Pretraining with Continuous Concepts—0
Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification—0
OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and Mask-like MechanismsCode0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers—0
Progressive Collaborative and Semantic Knowledge Fusion for Generative Recommendation—0
Rationalization Models for Text-to-SQL—0
Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge DistillationCode0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—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