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 1751–1800 of 4240 papers

TitleStatusHype
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Understanding the Gains from Repeated Self-Distillation—0
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates—0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners—0
Relative Difficulty Distillation for Semantic SegmentationCode0
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality AssessmentCode0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Accelerated Proton Resonance Frequency-based Magnetic Resonance Thermometry by Optimized Deep Learning MethodCode0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object DetectionCode0
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application—0
Self-Cooperation Knowledge Distillation for Novel Class Discovery—0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data RegimesCode0
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization—0
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly DetectionCode0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
Direct Preference Knowledge Distillation for Large Language Models—0
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Instance Temperature Knowledge DistillationCode0
Aligning Teacher with Student Preferences for Tailored Training Data Generation—0
On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge—0
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation—0
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—0
Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—0
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency—0
Exploring compressibility of transformer based text-to-music (TTM) models—0
Enhancing OOD Detection Using Latent DiffusionCode0
The Privileged Students: On the Value of Initialization in Multilingual Knowledge Distillation—0
Continual Learning with Diffusion-based Generative Replay for Industrial Streaming Data—0
Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning—0
Reinforced Knowledge Distillation for Time Series RegressionCode0
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices—0
Factual Dialogue Summarization via Learning from Large Language Models—0
SeCoKD: Aligning Large Language Models for In-Context Learning with Fewer Shots—0
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition—0
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge DistillationCode0
WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth EstimationCode0
Can Low-Rank Knowledge Distillation in LLMs be Useful for Microelectronic Reasoning?—0
Federated Learning with a Single Shared ImageCode0
Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation—0
Vernacular? I Barely Know Her: Challenges with Style Control and Stereotyping—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