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 1951–2000 of 4240 papers

TitleStatusHype
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding—0
GVP: Generative Volumetric Primitives—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation—0
Bilateral Memory Consolidation for Continual Learning—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
Improving Neural ODEs via Knowledge Distillation—0
Guided Deep Metric Learning—0
GTCOM Neural Machine Translation Systems for WMT19—0
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner—0
Improving Pronunciation and Accent Conversion through Knowledge Distillation And Synthetic Ground-Truth from Native TTS—0
Growing Deep Neural Network Considering with Similarity between Neurons—0
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space—0
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation—0
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning—0
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients—0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation—0
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—0
Debiased Distillation by Transplanting the Last Layer—0
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation—0
Grouped Knowledge Distillation for Deep Face Recognition—0
Group Distributionally Robust Knowledge Distillation—0
Improving Video Model Transfer With Dynamic Representation Learning—0
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement—0
Group channel pruning and spatial attention distilling for object detection—0
Improving Zero-Shot Multilingual Text Generation via Iterative Distillation—0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Dealing with training and test segmentation mismatch: FBK@IWSLT2021—0
Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance—0
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference—0
Incremental Knowledge Based Question Answering—0
Incremental Learning for End-to-End Automatic Speech Recognition—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
DDK: Distilling Domain Knowledge for Efficient Large Language Models—0
DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class—0
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation—0
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation—0
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision 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