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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 1801–1850 of 4240 papers

TitleStatusHype
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Deep-to-bottom Weights Decay: A Systemic Knowledge Review Learning Technique for Transformer Layers in Knowledge Distillation—0
Block-wise Intermediate Representation Training for Model Compression—0
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference—0
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection—0
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection—0
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review—0
Deep Neural Network Models Compression—0
Deep Neural Compression Via Concurrent Pruning and Self-Distillation—0
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation—0
Amortized Noisy Channel Neural Machine Translation—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Deep Net Triage: Analyzing the Importance of Network Layers via Structural Compression—0
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback—0
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack—0
HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs—0
HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks—0
Hint-dynamic Knowledge Distillation—0
Highly Constrained Coded Aperture Imaging Systems Design Via a Knowledge Distillation Approach—0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19—0
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers—0
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
Hierarchical Selective Classification—0
Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference—0
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling—0
Deep Collective Knowledge Distillation—0
A metric learning approach for endoscopic kidney stone identification—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Heterogeneous Generative Knowledge Distillation with Masked Image Modeling—0
High Performance Natural Language Processing—0
Heterogeneous Federated Learning Using Knowledge Codistillation—0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning—0
Heterogeneous Continual Learning—0
Heterogeneous-Branch Collaborative Learning for Dialogue Generation—0
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model—0
Adaptive Multiplane Image Generation from a Single Internet Picture—0
A Closer Look at Rehearsal-Free Continual Learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
HeteFedRec: Federated Recommender Systems with Model Heterogeneity—0
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment—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