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 1026–1050 of 4240 papers

TitleStatusHype
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark—0
BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation—0
Forward-Backward Knowledge Distillation for Continual Clustering—0
Continual Collaborative Distillation for Recommender SystemCode1
Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures—0
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
P4: Towards private, personalized, and Peer-to-Peer learning—0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
LoReTrack: Efficient and Accurate Low-Resolution Transformer TrackingCode1
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Rethinking Early-Fusion Strategies for Improved Multispectral Object DetectionCode1
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning—0
Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasetsCode0
3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous DrivingCode1
Pre-Trained Vision-Language Models as Partial Annotators—0
Recurrent Early Exits for Federated Learning with Heterogeneous ClientsCode1
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis ModelsCode1
Awesome Multi-modal Object TrackingCode5
Efficient Multitask Dense Predictor via BinarizationCode0
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge DistillationCode0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—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