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Data-free Knowledge Distillation

Papers

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Showing 1–50 of 75 papers

TitleStatusHype
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed EnvironmentsCode0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—0
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Does Training with Synthetic Data Truly Protect Privacy?Code0
Hybrid Data-Free Knowledge DistillationCode0
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
One-shot Federated Learning via Synthetic Distiller-Distillate CommunicationCode1
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion—0
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks—0
EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic DataCode1
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any ArchitectureCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification—0
Small Scale Data-Free Knowledge DistillationCode1
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data—0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
Data-free Knowledge Distillation for Fine-grained Visual CategorizationCode0
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge DistillationCode0
Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge DistillationCode0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous DataCode0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge DistillationCode1
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning—0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Prompt2Model: Generating Deployable Models from Natural Language InstructionsCode4
Sampling to Distill: Knowledge Transfer from Open-World Data—0
Mitigating Cross-client GANs-based Attack in Federated Learning—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Customizing Synthetic Data for Data-Free Student LearningCode0
Revisiting Data-Free Knowledge Distillation with Poisoned TeachersCode1
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?Code1
Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification—0
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Explicit and Implicit Knowledge Distillation via Unlabeled Data—0
Synthetic data generation method for data-free knowledge distillation in regression neural networksCode0
Data-Free Knowledge Distillation via Feature Exchange and Activation Region ConstraintCode1
Completely Heterogeneous Federated Learning—0
ProGen: Progressive Zero-shot Dataset Generation via In-context FeedbackCode1
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation—0
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning StrategyCode0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
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All datasetsQNLISQuAD

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GOLD (T5-base)Accuracy91.7—Unverified
2ZeroGen (T5-base)Accuracy88.5—Unverified
3ProGen (T5-base)Accuracy85.9—Unverified
4Prompt2Model (T5-base)Accuracy62.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GOLD (T5-base)Exact Match75.2—Unverified
2Prompt2Model (T5-base)Exact Match74.4—Unverified
3ZeroGen (T5-base)Exact Match69.4—Unverified
4ProGen (T5-base)Exact Match68.1—Unverified