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 4051–4100 of 4240 papers

TitleStatusHype
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
Low Resource Causal Event Detection from Biomedical Literature—0
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation—0
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding—0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition—0
LTD: Low Temperature Distillation for Robust Adversarial Training—0
M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning—0
MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations—0
Making Neural Machine Reading Comprehension Faster—0
Making Small Language Models Better Few-Shot Learners—0
Mamba base PKD for efficient knowledge compression—0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation—0
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems—0
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation—0
Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding—0
Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation—0
Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management—0
Masked Autoencoders Are Stronger Knowledge Distillers—0
The Role of Masking for Efficient Supervised Knowledge Distillation of Vision Transformers—0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation—0
Maximizing Discrimination Capability of Knowledge Distillation with Energy Function—0
Maximum Likelihood Distillation for Robust Modulation Classification—0
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning—0
Enhancing Metaphor Detection through Soft Labels and Target Word Prediction—0
Measuring and Reducing Model Update Regression in Structured Prediction for NLP—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
MEDIC: Remove Model Backdoors via Importance Driven Cloning—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models—0
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation—0
MentalMAC: Enhancing Large Language Models for Detecting Mental Manipulation via Multi-Task Anti-Curriculum Distillation—0
MergeDistill: Merging Pre-trained Language Models using Distillation—0
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities—0
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation—0
Meta-Ensemble Parameter Learning—0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
Meta Knowledge Distillation—0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
MetaMixer: A Regularization Strategy for Online Knowledge Distillation—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members—0
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression—0
Microdosing: Knowledge Distillation for GAN based Compression—0
Microsoft Research Asia's Systems for WMT19—0
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery—0
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—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