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 1726–1750 of 4240 papers

TitleStatusHype
Multi-Label Knowledge DistillationCode1
Continual Face Forgery Detection via Historical Distribution Preserving—0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic FeaturesCode0
Towards General and Fast Video Derain via Knowledge Distillation—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA—0
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPRCode1
AICSD: Adaptive Inter-Class Similarity Distillation for Semantic SegmentationCode1
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated DataCode2
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge DistillationCode0
Teacher-Student Architecture for Knowledge Distillation: A Survey—0
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization—0
Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge DistillationCode0
Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge DistillationCode0
One-stage Low-resolution Text Recognition with High-resolution Knowledge TransferCode1
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across CitiesCode1
Scene-aware Human Pose Generation using Transformer—0
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPsCode1
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning—0
Eyelid’s Intrinsic Motion-aware Feature Learning for Real-time Eyeblink Detection in the WildCode0
Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penaltyCode1
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models—0
Spatio-Temporal Branching for Motion Prediction using Motion IncrementsCode0
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search—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