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 2901–2950 of 4240 papers

TitleStatusHype
Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—0
Towards Making Deep Transfer Learning Never Hurt—0
Towards Model Agnostic Federated Learning Using Knowledge Distillation—0
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation—0
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images—0
Towards Optimal Trade-offs in Knowledge Distillation for CNNs and Vision Transformers at the Edge—0
Towards Oracle Knowledge Distillation with Neural Architecture Search—0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
Towards Robust Classification with Image Quality Assessment—0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach—0
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition—0
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation—0
Towards Streaming Egocentric Action Anticipation—0
SOCRATES: Text-based Human Search and Approach using a Robot Dog—0
Towards Unconstrained 2D Pose Estimation of the Human Spine—0
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning—0
Towards Understanding Knowledge Distillation—0
Do we need Label Regularization to Fine-tune Pre-trained Language Models?—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Toxicity Detection can be Sensitive to the Conversational Context—0
Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
Training Domain Draft Models for Speculative Decoding: Best Practices and Insights—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks—0
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification—0
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction—0
Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption—0
Transfer Learning with Pre-trained Conditional Generative Models—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Transferring Learning Trajectories of Neural Networks—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation—0
Tree Knowledge Distillation for Compressing Transformer-Based Language Models—0
Tree-Like Decision Distillation—0
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology—0
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction—0
TRILLsson: Distilled Universal Paralinguistic Speech Representations—0
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning—0
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching—0
Triplet Knowledge Distillation—0
Triple-View Knowledge Distillation for Semi-Supervised 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