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 3501–3550 of 4240 papers

TitleStatusHype
Follow Your Path: a Progressive Method for Knowledge Distillation—0
For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction—0
Forward-Backward Knowledge Distillation for Continual Clustering—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
From LLM to NMT: Advancing Low-Resource Machine Translation with Claude—0
From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation—0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation—0
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding—0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners—0
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage—0
Future-Guided Incremental Transformer for Simultaneous Translation—0
Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK—0
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning—0
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models—0
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning—0
GAN-Knowledge Distillation for one-stage Object Detection—0
Gap Preserving Distillation by Building Bidirectional Mappings with A Dynamic Teacher—0
GazeGen: Gaze-Driven User Interaction for Visual Content Generation—0
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation—0
GenDistiller: Distilling Pre-trained Language Models based on Generative Models—0
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—0
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics—0
Generalized Continual Zero-Shot Learning—0
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications—0
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation—0
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation—0
Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning—0
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings—0
Generative Adversarial Simulator—0
Generative Dataset Distillation Based on Self-knowledge Distillation—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
GenURL: A General Framework for Unsupervised Representation Learning—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts—0
GhostNetV3: Exploring the Training Strategies for Compact Models—0
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data—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