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 1601–1650 of 4240 papers

TitleStatusHype
Distilling Reasoning Capabilities into Smaller Language ModelsCode0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
Distilling Model KnowledgeCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Class incremental learning with probability dampening and cascaded gated classifierCode0
Robust Model Compression Using Deep HypothesesCode0
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data RegimesCode0
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and FusionCode0
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric LearningCode0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum TrainingCode0
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
Guiding Frame-Level CTC Alignments Using Self-knowledge DistillationCode0
CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation LossCode0
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
Group Multi-View Transformer for 3D Shape Analysis with Spatial EncodingCode0
Highlight Every Step: Knowledge Distillation via Collaborative TeachingCode0
CAPEEN: Image Captioning with Early Exits and Knowledge DistillationCode0
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain GeneralizationCode0
Graph Knowledge Distillation to Mixture of ExpertsCode0
SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillationCode0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
Graph Entropy Minimization for Semi-supervised Node ClassificationCode0
Scaffolding a Student to Instill KnowledgeCode0
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence EmbeddingCode0
Gradient Knowledge Distillation for Pre-trained Language ModelsCode0
Distilling Knowledge for Empathy DetectionCode0
Distilling Knowledge for Designing Computational Imaging SystemsCode0
GOTHAM: Graph Class Incremental Learning Framework under Weak SupervisionCode0
Spending Your Winning Lottery Better After Drawing ItCode0
Goldfish: An Efficient Federated Unlearning FrameworkCode0
Distilling Influences to Mitigate Prediction Churn in Graph Neural NetworksCode0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Goal-Conditioned Q-Learning as Knowledge DistillationCode0
Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge DistillationCode0
Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue GenerationCode0
Distilling Image Dehazing With Heterogeneous Task ImitationCode0
GNN's Uncertainty Quantification using Self-DistillationCode0
GLANCE: Global to Local Architecture-Neutral Concept-based ExplanationsCode0
GLiRA: Black-Box Membership Inference Attack via Knowledge DistillationCode0
Distilling Global and Local Logits With Densely Connected RelationsCode0
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent InferenceCode0
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM DeploymentCode0
Camera-Incremental Object Re-Identification with Identity Knowledge EvolutionCode0
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense PredictionCode0
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language ProcessingCode0
Annealing Knowledge DistillationCode0
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and RegressionCode0
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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