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 926–950 of 4240 papers

TitleStatusHype
Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object DetectionCode0
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application—0
Self-Cooperation Knowledge Distillation for Novel Class Discovery—0
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data RegimesCode0
AdaDistill: Adaptive Knowledge Distillation for Deep Face RecognitionCode1
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization—0
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly DetectionCode0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Direct Preference Knowledge Distillation for Large Language Models—0
Instance Temperature Knowledge DistillationCode0
Aligning Teacher with Student Preferences for Tailored Training Data Generation—0
On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks—0
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—0
Sequential Editing for Lifelong Training of Speech Recognition Models—0
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation—0
Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels—0
MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language NavigationCode1
Dual-Space Knowledge Distillation for Large Language ModelsCode2
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation—0
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression RecognitionCode1
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—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