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 2151–2200 of 4240 papers

TitleStatusHype
Efficient Image Compression Using Advanced State Space Models—0
Efficient Inference via Universal LSH Kernel—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights—0
Efficient Knowledge Distillation of SAM for Medical Image Segmentation—0
Efficient Knowledge Distillation via Curriculum Extraction—0
Efficient Machine Translation with Model Pruning and Quantization—0
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation—0
Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation—0
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation—0
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Efficient Verified Machine Unlearning For Distillation—0
Efficient Video Segmentation Models with Per-frame Inference—0
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss—0
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection—0
IOR: Inversed Objects Replay for Incremental Object Detection—0
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification—0
Knowledge distillation for optimization of quantized deep neural networks—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning—0
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder—0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022—0
End-to-End Speech Translation with Knowledge Distillation—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
Enhanced Sparsification via Stimulative Training—0
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation—0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—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