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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 2251–2300 of 4240 papers

TitleStatusHype
Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning—0
Explaining Knowledge Distillation by Quantifying the Knowledge—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
Explicit and Implicit Knowledge Distillation via Unlabeled Data—0
Explicit Connection Distillation—0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR—0
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models—0
Exploring compressibility of transformer based text-to-music (TTM) models—0
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Exploring Extreme Quantization in Spiking Language Models—0
Exploring Knowledge Distillation of a Deep Neural Network for Multi-Script identification—0
Fully Synthetic Data Improves Neural Machine Translation with Knowledge Distillation—0
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection—0
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
A Note on Knowledge Distillation Loss Function for Object Classification—0
Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification with DocBERT—0
Extending Label Smoothing Regularization with Self-Knowledge Distillation—0
Extracting General-use Transformers for Low-resource Languages via Knowledge Distillation—0
Extracting knowledge from features with multilevel abstraction—0
Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Extreme Compression for Pre-trained Transformers Made Simple and Efficient—0
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Extremely Small BERT Models from Mixed-Vocabulary Training—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Factorized Distillation: Training Holistic Person Re-identification Model by Distilling an Ensemble of Partial ReID Models—0
Factorized RVQ-GAN For Disentangled Speech Tokenization—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Selective Cross-Task Distillation—0
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices—0
Fair Feature Distillation for Visual Recognition—0
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates—0
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning—0
Faithful Knowledge Distillation—0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation—0
Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection—0
Fast and Efficient Once-For-All Networks for Diverse Hardware Deployment—0
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation—0
Fast DistilBERT on CPUs—0
Fast End-to-end Coreference Resolution for Korean—0
FasterAI: A Lightweight Library for Creating Sparse Neural Networks—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—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