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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 1651–1700 of 4240 papers

TitleStatusHype
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
After-Stroke Arm Paresis Detection using Kinematic Data—0
Fixing the Teacher-Student Knowledge Discrepancy in Distillation—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition—0
FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning—0
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
End-to-End Speech Translation with Knowledge Distillation—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022—0
Follow Your Path: a Progressive Method for Knowledge Distillation—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation—0
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder—0
Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning—0
Compositional Data Augmentation for Abstractive Conversation Summarization—0
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—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
Adaptive Explicit Knowledge Transfer for Knowledge Distillation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—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
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again—0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation—0
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—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
High Performance Natural Language Processing—0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval—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