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

Showing 726750 of 4240 papers

TitleStatusHype
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Distilling Holistic Knowledge with Graph Neural NetworksCode1
Transferring Knowledge Distillation for Multilingual Social Event DetectionCode1
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
Learning Compatible EmbeddingsCode1
Online Knowledge Distillation for Efficient Pose EstimationCode1
Hierarchical Self-supervised Augmented Knowledge DistillationCode1
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image ClassificationCode1
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural NetworkCode1
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental LearningCode1
Structured Sparse R-CNN for Direct Scene Graph GenerationCode1
Context-Aware Image Inpainting with Learned Semantic PriorsCode1
Does Knowledge Distillation Really Work?Code1
Distilling Image Classifiers in Object DetectorsCode1
BERT Learns to Teach: Knowledge Distillation with Meta LearningCode1
XtremeDistilTransformers: Task Transfer for Task-agnostic DistillationCode1
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box ModelCode1
Preservation of the Global Knowledge by Not-True Distillation in Federated LearningCode1
Bidirectional Distillation for Top-K Recommender SystemCode1
Towards Quantifiable Dialogue Coherence EvaluationCode1
Transformer-Based Source-Free Domain AdaptationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43Unverified