ShiftKD: Benchmarking Knowledge Distillation under Distribution Shift
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhang, Songming, Luo, Yuxiao, Lyu, Ziyu, Chen, Xiaofeng |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
por: Guan, Yushi, et al.
Publicado: (2026)
por: Guan, Yushi, et al.
Publicado: (2026)
Benchmarking Distribution Shift in Tabular Data with TableShift
por: Gardner, Josh, et al.
Publicado: (2023)
por: Gardner, Josh, et al.
Publicado: (2023)
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
por: Zhang, Songming, et al.
Publicado: (2023)
por: Zhang, Songming, et al.
Publicado: (2023)
Graphs Generalization under Distribution Shifts
por: Tian, Qin, et al.
Publicado: (2024)
por: Tian, Qin, et al.
Publicado: (2024)
BicKD: Bilateral Contrastive Knowledge Distillation
por: Zhu, Jiangnan, et al.
Publicado: (2026)
por: Zhu, Jiangnan, et al.
Publicado: (2026)
HyperKD: Distilling Cross-Spectral Knowledge in Masked Autoencoders via Inverse Domain Shift with Spatial-Aware Masking and Specialized Loss
por: Matin, Abdul, et al.
Publicado: (2025)
por: Matin, Abdul, et al.
Publicado: (2025)
Trustworthy Machine Learning under Distribution Shifts
por: Huang, Zhuo
Publicado: (2025)
por: Huang, Zhuo
Publicado: (2025)
Optimal Classification under Performative Distribution Shift
por: Cyffers, Edwige, et al.
Publicado: (2024)
por: Cyffers, Edwige, et al.
Publicado: (2024)
FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings
por: Nguyen, Quang-Huy, et al.
Publicado: (2026)
por: Nguyen, Quang-Huy, et al.
Publicado: (2026)
S^2-KD: Semantic-Spectral Knowledge Distillation Spatiotemporal Forecasting
por: Wang, Wenshuo, et al.
Publicado: (2025)
por: Wang, Wenshuo, et al.
Publicado: (2025)
Label Alignment Regularization for Distribution Shift
por: Imani, Ehsan, et al.
Publicado: (2022)
por: Imani, Ehsan, et al.
Publicado: (2022)
FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning
por: Ku, Eugene
Publicado: (2024)
por: Ku, Eugene
Publicado: (2024)
RMT-KD: Random Matrix Theoretic Causal Knowledge Distillation
por: Ettori, Davide, et al.
Publicado: (2025)
por: Ettori, Davide, et al.
Publicado: (2025)
Wasserstein-regularized Conformal Prediction under General Distribution Shift
por: Xu, Rui, et al.
Publicado: (2025)
por: Xu, Rui, et al.
Publicado: (2025)
Mitigating Distribution Shift in Model-based Offline RL via Shifts-aware Reward Learning
por: Luo, Wang, et al.
Publicado: (2024)
por: Luo, Wang, et al.
Publicado: (2024)
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
por: Xie, Renchunzi, et al.
Publicado: (2024)
por: Xie, Renchunzi, et al.
Publicado: (2024)
Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs
por: Xu, Xiaoyang, et al.
Publicado: (2025)
por: Xu, Xiaoyang, et al.
Publicado: (2025)
Adaptive Estimation and Learning under Temporal Distribution Shift
por: Baby, Dheeraj, et al.
Publicado: (2025)
por: Baby, Dheeraj, et al.
Publicado: (2025)
Distributionally Robust Coreset Selection under Covariate Shift
por: Tanaka, Tomonari, et al.
Publicado: (2025)
por: Tanaka, Tomonari, et al.
Publicado: (2025)
Weak-to-Strong Generalization under Distribution Shifts
por: Jeon, Myeongho, et al.
Publicado: (2025)
por: Jeon, Myeongho, et al.
Publicado: (2025)
Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
por: Sharma, Arun, et al.
Publicado: (2025)
por: Sharma, Arun, et al.
Publicado: (2025)
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
por: Zou, Deyu, et al.
Publicado: (2023)
por: Zou, Deyu, et al.
Publicado: (2023)
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
por: Zhang, Songming, et al.
Publicado: (2026)
por: Zhang, Songming, et al.
Publicado: (2026)
MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
por: Zheng, Yuepeng, et al.
Publicado: (2025)
por: Zheng, Yuepeng, et al.
Publicado: (2025)
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
por: Zhang, Zeyang, et al.
Publicado: (2024)
por: Zhang, Zeyang, et al.
Publicado: (2024)
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
por: Xu, Haotian, et al.
Publicado: (2025)
por: Xu, Haotian, et al.
Publicado: (2025)
Distributionally Robust Safe Sample Elimination under Covariate Shift
por: Hanada, Hiroyuki, et al.
Publicado: (2024)
por: Hanada, Hiroyuki, et al.
Publicado: (2024)
Federated Learning with Profile Mapping under Distribution Shifts and Drifts
por: Li, Mohan, et al.
Publicado: (2026)
por: Li, Mohan, et al.
Publicado: (2026)
Safe Distributionally Robust Feature Selection under Covariate Shift
por: Hanada, Hiroyuki, et al.
Publicado: (2026)
por: Hanada, Hiroyuki, et al.
Publicado: (2026)
Bias as a Virtue: Rethinking Generalization under Distribution Shifts
por: Chen, Ruixuan, et al.
Publicado: (2025)
por: Chen, Ruixuan, et al.
Publicado: (2025)
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
por: Zhang, Kexin, et al.
Publicado: (2024)
por: Zhang, Kexin, et al.
Publicado: (2024)
DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
por: Sun, Guiquan, et al.
Publicado: (2026)
por: Sun, Guiquan, et al.
Publicado: (2026)
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
por: Qin, Dalin, et al.
Publicado: (2024)
por: Qin, Dalin, et al.
Publicado: (2024)
A Dual-Space Framework for General Knowledge Distillation of Large Language Models
por: Zhang, Xue, et al.
Publicado: (2025)
por: Zhang, Xue, et al.
Publicado: (2025)
MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift
por: Neo, Dexter, et al.
Publicado: (2023)
por: Neo, Dexter, et al.
Publicado: (2023)
OCRR: A Benchmark for Online Correction Recovery under Distribution Shift
por: Grassi, Adrian
Publicado: (2026)
por: Grassi, Adrian
Publicado: (2026)
Causal-aware Graph Neural Architecture Search under Distribution Shifts
por: Li, Peiwen, et al.
Publicado: (2024)
por: Li, Peiwen, et al.
Publicado: (2024)
Optimal Empirical Risk Minimization under Temporal Distribution Shifts
por: Jeong, Yujin, et al.
Publicado: (2025)
por: Jeong, Yujin, et al.
Publicado: (2025)
Mean-Shift Distillation for Diffusion Mode Seeking
por: Thamizharasan, Vikas, et al.
Publicado: (2025)
por: Thamizharasan, Vikas, et al.
Publicado: (2025)
ODD: Overlap-aware Estimation of Model Performance under Distribution Shift
por: Mishra, Aayush, et al.
Publicado: (2025)
por: Mishra, Aayush, et al.
Publicado: (2025)
Ejemplares similares
-
TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
por: Guan, Yushi, et al.
Publicado: (2026) -
Benchmarking Distribution Shift in Tabular Data with TableShift
por: Gardner, Josh, et al.
Publicado: (2023) -
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
por: Zhang, Songming, et al.
Publicado: (2023) -
Graphs Generalization under Distribution Shifts
por: Tian, Qin, et al.
Publicado: (2024) -
BicKD: Bilateral Contrastive Knowledge Distillation
por: Zhu, Jiangnan, et al.
Publicado: (2026)