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
Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix—0
Multimodal Locally Enhanced Transformer for Continuous Sign Language Recognition—0
Multimodal Prescriptive Deep Learning—0
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation—0
Multi-Person Full Body Pose Estimation—0
Multi-perspective Contrastive Logit Distillation—0
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation—0
Multi scale Feature Extraction and Fusion for Online Knowledge Distillation—0
Learning to Purification for Unsupervised Person Re-identification—0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension—0
Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator—0
Multi-Task Learning with Knowledge Distillation for Dense Prediction—0
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification—0
Multivariate Prototype Representation for Domain-Generalized Incremental Learning—0
Multi-View Attention Transfer for Efficient Speech Enhancement—0
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation—0
Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization—0
Multi-view knowledge distillation transformer for human action recognition—0
MUSE: Feature Self-Distillation with Mutual Information and Self-Information—0
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition—0
Mutual Adversarial Training: Learning together is better than going alone—0
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders—0
Mutual Learning for Finetuning Click-Through Rate Prediction Models—0
Mutual-Learning Improves End-to-End Speech Translation—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning—0
MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring—0
NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task—0
Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting—0
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions—0
Natural Statistics of Network Activations and Implications for Knowledge Distillation—0
Nearest Neighbor Knowledge Distillation for Neural Machine Translation—0
Neighbourhood Distillation: On the benefits of non end-to-end distillation—0
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks—0
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks—0
Network-Agnostic Knowledge Transfer for Medical Image Segmentation—0
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models—0
Neural Architecture Search via Ensemble-based Knowledge Distillation—0
Neural Collapse Inspired Knowledge Distillation—0
Neural Compatibility Modeling with Attentive Knowledge Distillation—0
Neural Machine Translation from Simplified Translations—0
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge—0
New Perspective on Progressive GANs Distillation for One-class Novelty Detection—0
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application—0
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation—0
Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation—0
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging—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