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 3301–3350 of 4240 papers

TitleStatusHype
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
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation—0
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation—0
Enhancing CTC-Based Visual Speech Recognition—0
Enhancing Data-Free Adversarial Distillation with Activation Regularization and Virtual Interpolation—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Enhancing Generalization in Chain of Thought Reasoning for Smaller Models—0
Enhancing Mapless Trajectory Prediction through Knowledge Distillation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning—0
Enhancing Semi-supervised Learning with Zero-shot Pseudolabels—0
Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation—0
Enhancing SLM via ChatGPT and Dataset Augmentation—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