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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 1901–1950 of 4240 papers

TitleStatusHype
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
A Survey on Transformer Compression—0
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer—0
Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss—0
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices—0
Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing—0
Impossible Triangle: What's Next for Pre-trained Language Models?—0
基于层间知识蒸馏的神经机器翻译(Inter-layer Knowledge Distillation for Neural Machine Translation)—0
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation—0
A Survey on Symbolic Knowledge Distillation of Large Language Models—0
Improved Customer Transaction Classification using Semi-Supervised Knowledge Distillation—0
A Flexible Multi-Task Model for BERT Serving—0
Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery—0
Improved knowledge distillation by utilizing backward pass knowledge in neural networks—0
Designing Parameter and Compute Efficient Diffusion Transformers using Distillation—0
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection—0
Improved Knowledge Distillation via Adversarial Collaboration—0
Joint Architecture and Knowledge Distillation in CNN for Chinese Text Recognition—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
A Survey on Recent Teacher-student Learning Studies—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Batch Selection and Communication for Active Learning with Edge Labeling—0
Improve Knowledge Distillation via Label Revision and Data Selection—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Improving Apple Object Detection with Occlusion-Enhanced Distillation—0
Improving Autoregressive NMT with Non-Autoregressive Model—0
Improving CLIP Robustness with Knowledge Distillation and Self-Training—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection—0
Improving Defensive Distillation using Teacher Assistant—0
Improving De-Raining Generalization via Neural Reorganization—0
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation—0
CoMBO: Conflict Mitigation via Branched Optimization for Class Incremental Segmentation—0
A Survey on Model Compression for Large Language Models—0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning—0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
Efficient Machine Translation with Model Pruning and Quantization—0
Noise as a Resource for Learning in Knowledge Distillation—0
Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging—0
Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning—0
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression—0
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—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