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 1401–1450 of 4240 papers

TitleStatusHype
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes—0
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher—0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
FedRAD: Federated Robust Adaptive Distillation—0
Ensemble Knowledge Distillation for CTR Prediction—0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy—0
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic—0
Conditional Autoregressors are Interpretable Classifiers—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation—0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
Ensemble Distillation for Neural Machine Translation—0
Conditional Generative Data-free Knowledge Distillation—0
Enhancing SLM via ChatGPT and Dataset Augmentation—0
Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation—0
Ensemble knowledge distillation of self-supervised speech models—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Enhancing Semi-supervised Learning with Zero-shot Pseudolabels—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
ConceptDistil: Model-Agnostic Distillation of Concept Explanations—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
Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval—0
Conformer with dual-mode chunked attention for joint online and offline ASR—0
Error Exponent in Agnostic PAC Learning—0
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
ESPnet-ST IWSLT 2021 Offline Speech Translation System—0
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits—0
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
A Transformer-in-Transformer Network Utilizing Knowledge Distillation for Image Recognition—0
Federated Learning for Data and Model Heterogeneity in Medical Imaging—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Mapless Trajectory Prediction through Knowledge Distillation—0
Compression of end-to-end non-autoregressive image-to-speech system for low-resourced devices—0
Federated Learning on Non-iid Data via Local and Global Distillation—0
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation—0
Compression of Deep Learning Models for Text: A Survey—0
Generalized Supervised Contrastive Learning—0
Compression of Acoustic Event Detection Models With Quantized Distillation—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—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