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 3351–3400 of 4240 papers

TitleStatusHype
LTD: Low Temperature Distillation for Robust Adversarial Training—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Knowledge Cross-Distillation for Membership Privacy—0
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksCode0
Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation—0
deepQuest-py: Large and Distilled Models for Quality EstimationCode0
Papago’s Submission for the WMT21 Quality Estimation Shared Task—0
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task—0
HW-TSC’s Participation in the WMT 2021 News Translation Shared Task—0
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification—0
Limitations of Knowledge Distillation for Zero-shot Transfer Learning—0
The NiuTrans System for the WMT 2021 Efficiency Task—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition—0
Mutual-Learning Improves End-to-End Speech Translation—0
The Mininglamp Machine Translation System for WMT21—0
Exploring Non-Autoregressive Text Style TransferCode0
The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task—0
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?—0
AUTOSUMM: Automatic Model Creation for Text Summarization—0
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation—0
TenTrans Large-Scale Multilingual Machine Translation System for WMT21—0
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning—0
Improving Stance Detection with Multi-Dataset Learning and Knowledge DistillationCode0
Efficient Machine Translation with Model Pruning and Quantization—0
NVIDIA NeMo’s Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21—0
PP-ShiTu: A Practical Lightweight Image Recognition SystemCode0
Distilling Knowledge for Empathy DetectionCode0
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style TransferCode0
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation—0
Rethinking the Knowledge Distillation From the Perspective of Model Calibration—0
Estimating and Maximizing Mutual Information for Knowledge Distillation—0
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks—0
Towards Model Agnostic Federated Learning Using Knowledge Distillation—0
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM—0
Temporal Knowledge Distillation for On-device Audio Classification—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Beyond Classification: Knowledge Distillation using Multi-Object Impressions—0
Response-based Distillation for Incremental Object Detection—0
MUSE: Feature Self-Distillation with Mutual Information and Self-Information—0
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Class Incremental Online Streaming Learning—0
Knowledge distillation from language model to acoustic model: a hierarchical multi-task learning approach—0
FedHe: Heterogeneous Models and Communication-Efficient Federated LearningCode0
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient DistillationCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
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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