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 901–950 of 4240 papers

TitleStatusHype
Enhancing Low-Resource NMT with a Multilingual Encoder and Knowledge Distillation: A Case StudyCode0
Reprogramming Distillation for Medical Foundation ModelsCode0
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction—0
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training—0
Leveraging Topological Guidance for Improved Knowledge DistillationCode0
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data—0
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language ModelsCode2
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates—0
Understanding the Gains from Repeated Self-Distillation—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality AssessmentCode0
Relative Difficulty Distillation for Semantic SegmentationCode0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge DistillationCode2
Accelerated Proton Resonance Frequency-based Magnetic Resonance Thermometry by Optimized Deep Learning MethodCode0
A Unified Framework for 3D Scene UnderstandingCode2
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—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
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data RegimesCode0
AdaDistill: Adaptive Knowledge Distillation for Deep Face RecognitionCode1
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
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Direct Preference Knowledge Distillation for Large Language Models—0
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
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation—0
Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels—0
MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language NavigationCode1
Dual-Space Knowledge Distillation for Large Language ModelsCode2
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression RecognitionCode1
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—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