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 2401–2450 of 4240 papers

TitleStatusHype
Understanding and Improving Knowledge Distillation—0
Understanding and Improving Lexical Choice in Non-Autoregressive Translation—0
Understanding Knowledge Distillation—0
Understanding Knowledge Distillation in Non-autoregressive Machine Translation—0
Understanding the Effect of Data Augmentation on Knowledge Distillation—0
Understanding the Gains from Repeated Self-Distillation—0
Understanding the Overfitting of the Episodic Meta-training—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
UNDO: Understanding Distillation as Optimization—0
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation—0
UNIDEAL: Curriculum Knowledge Distillation Federated Learning—0
Unified and Effective Ensemble Knowledge Distillation—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Unified Attacks to Large Language Model Watermarks: Spoofing and Scrubbing in Unauthorized Knowledge Distillation—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
UniKD: Universal Knowledge Distillation for Mimicking Homogeneous or Heterogeneous Object Detectors—0
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion—0
UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation—0
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search—0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling—0
Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation—0
Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer—0
Unlearning Clients, Features and Samples in Vertical Federated Learning—0
Unlearning via Sparse Representations—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Unlimited Knowledge Distillation for Action Recognition in the Dark—0
Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing—0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer—0
Unraveling Key Factors of Knowledge Distillation—0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving—0
Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation—0
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model—0
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)—0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions—0
Unveiling Context-Aware Criteria in Self-Assessing LLMs—0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion—0
Using Advanced LLMs to Enhance Smaller LLMs: An Interpretable Knowledge Distillation Approach—0
Using a GAN to Generate Adversarial Examples to Facial Image Recognition—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Using Knowledge Distillation to improve interpretable models in a retail banking context—0
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation—0
Using the Past Knowledge to Improve Sentiment Classification—0
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models—0
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training—0
Variational Information Distillation for Knowledge Transfer—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