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 1251–1275 of 4240 papers

TitleStatusHype
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector—0
ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Data—0
BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation—0
Multilingual Non-Autoregressive Machine Translation without Knowledge DistillationCode0
Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
A Unified Knowledge-Distillation and Semi-Supervised Learning Framework to Improve Industrial Ads Delivery Systems—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model—0
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy DistillationCode0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design—0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—0
Distilling Knowledge for Designing Computational Imaging SystemsCode0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Target-driven Self-Distillation for Partial Observed Trajectories Forecasting—0
Efficient Knowledge Distillation of SAM for Medical Image Segmentation—0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
A Contrastive Teacher-Student Framework for Novelty Detection under Style Shifts—0
FedEFM: Federated Endovascular Foundation Model with Unseen Data—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