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 3101–3150 of 4240 papers

TitleStatusHype
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models—0
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices—0
DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection—0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
DiagrammaticLearning: A Graphical Language for Compositional Training Regimes—0
Dialect Identification through Adversarial Learning and Knowledge Distillation on Romanian BERT—0
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning—0
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
Digging Deeper into CRNN Model in Chinese Text Images Recognition—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
DILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems—0
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling—0
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs—0
Direct Distillation between Different Domains—0
Direct Preference Knowledge Distillation for Large Language Models—0
DiReDi: Distillation and Reverse Distillation for AIoT Applications—0
Disentanglement, Visualization and Analysis of Complex Features in DNNs—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning—0
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation—0
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation—0
Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models—0
Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks—0
Distillation-Enhanced Physical Adversarial Attacks—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Distillation of Diffusion Features for Semantic Correspondence—0
Distillation of Human-Object Interaction Contexts for Action Recognition—0
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning—0
Distilling 3D distinctive local descriptors for 6D pose estimation—0
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
Distilling CLIP with Dual Guidance for Learning Discriminative Human Body Shape Representation—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition—0
Distilling EEG Representations via Capsules for Affective Computing—0
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation—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