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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 2201–2250 of 4240 papers

TitleStatusHype
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
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic—0
Ensemble Knowledge Distillation for CTR Prediction—0
Ensemble Distillation for Neural Machine Translation—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Ensemble knowledge distillation of self-supervised speech models—0
Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs—0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction—0
EPIK: Eliminating multi-model Pipelines with Knowledge-distillation—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval—0
Error Exponent in Agnostic PAC Learning—0
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining—0
ESPnet How2 Speech Translation System for IWSLT 2019: Pre-training, Knowledge Distillation, and Going Deeper—0
ESPnet-ST IWSLT 2021 Offline Speech Translation System—0
Essence Knowledge Distillation for Speech Recognition—0
Estimating and Maximizing Mutual Information for Knowledge Distillation—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models—0
Evidential Federated Learning for Skin Lesion Image Classification—0
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation—0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Evolving Storytelling: Benchmarks and Methods for New Character Customization with Diffusion Models—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition—0
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders—0
Experimentation in Content Moderation using RWKV—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—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