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 2051–2100 of 4240 papers

TitleStatusHype
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions—0
PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction ModelsCode0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Induced Model Matching: How Restricted Models Can Help Larger OnesCode0
On the Byzantine-Resilience of Distillation-Based Federated LearningCode0
Revisiting Knowledge Distillation for Autoregressive Language ModelsCode0
Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge DistillationCode0
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Cultural Commonsense Knowledge for Intercultural Dialogues—0
Model Compression and Efficient Inference for Large Language Models: A Survey—0
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language ModelsCode0
Distilled Gradual Pruning with Pruned Fine-tuningCode0
Walsh-domain Neural Network for Power Amplifier Behavioral Modelling and Digital Predistortion—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies—0
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated LearningCode0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive DistillationCode0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss—0
Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge DistillationCode0
Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning—0
A Survey on Transformer Compression—0
Dual Knowledge Distillation for Efficient Sound Event Detection—0
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference—0
Cooperative Knowledge Distillation: A Learner Agnostic ApproachCode0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection—0
Class incremental learning with probability dampening and cascaded gated classifierCode0
Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Scavenging Hyena: Distilling Transformers into Long Convolution Models—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
Stolen Subwords: Importance of Vocabularies for Machine Translation Model StealingCode0
TQCompressor: improving tensor decomposition methods in neural networks via permutationsCode0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection—0
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillationCode0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction—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