Imagen de Portada

On robust overfitting: adversarial training induced distribution matters

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tian, Runzhi, Mao, Yongyi
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
por: Shi, Zhongjie, et al.
Publicado: (2024)

Enhancing robustness of data-driven SHM models: adversarial training with circle loss
por: Yang, Xiangli, et al.
Publicado: (2024)

Provable tradeoffs in adversarially robust classification
por: Dobriban, Edgar, et al.
Publicado: (2020)

Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
por: Ballarin, Emanuele, et al.
Publicado: (2023)

The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
por: Hassani, Hamed, et al.
Publicado: (2022)

Nonideality-aware training makes memristive networks more robust to adversarial attacks
por: Joksas, Dovydas, et al.
Publicado: (2024)

Why you don't overfit, and don't need Bayes if you only train for one epoch
por: Aitchison, Laurence
Publicado: (2024)

Can Go AIs be adversarially robust?
por: Tseng, Tom, et al.
Publicado: (2024)

On damage of interpolation to adversarial robustness in regression
por: Peng, Jingfu, et al.
Publicado: (2026)

Be aware of overfitting by hyperparameter optimization!
por: Tetko, Igor V., et al.
Publicado: (2024)

ProARD: progressive adversarial robustness distillation: provide wide range of robust students
por: Mousavi, Seyedhamidreza, et al.
Publicado: (2025)

Data-driven transport modelling without overfit
por: Vanya, Peter, et al.
Publicado: (2026)

Wasserstein distributional adversarial training for deep neural networks
por: Bai, Xingjian, et al.
Publicado: (2025)

A unifying Bayesian framework for adversarial robustness
por: Arce, Pablo G., et al.
Publicado: (2025)

Parallel Recursive LSTM
por: Gaudreault, Tristan, et al.
Publicado: (2026)

On adversarial training and the 1 Nearest Neighbor classifier
por: Hagai, Amir, et al.
Publicado: (2024)

Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG
por: Guo, Manshan, et al.
Publicado: (2024)

Conservative neural posterior estimation via distributionally robust training
por: Laplante, William, et al.
Publicado: (2026)

P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors
por: David, Lucas, et al.
Publicado: (2023)

Does simple trump complex? Comparing strategies for adversarial robustness in DNNs
por: Brooks, William, et al.
Publicado: (2025)

Spectral regularization for adversarially-robust representation learning
por: Yang, Sheng, et al.
Publicado: (2024)

Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States
por: Wang, Ziqiao, et al.
Publicado: (2022)

Generalization Bounds via Conditional $f$-Information
por: Wang, Ziqiao, et al.
Publicado: (2024)

First line of defense: A robust first layer mitigates adversarial attacks
por: Suresh, Janani, et al.
Publicado: (2024)

Are classical deep neural networks weakly adversarially robust?
por: Sun, Nuolin, et al.
Publicado: (2025)

Preventing overfitting in deep learning using differential privacy
por: Khatri, Alizishaan Anwar Hussein
Publicado: (2026)

The temporal overfitting problem with applications in wind power curve modeling
por: Prakash, Abhinav, et al.
Publicado: (2020)

Benign overfitting in leaky ReLU networks with moderate input dimension
por: Karhadkar, Kedar, et al.
Publicado: (2024)

On $f$-Divergence Principled Domain Adaptation: An Improved Framework
por: Wang, Ziqiao, et al.
Publicado: (2024)

Deep MMD Gradient Flow without adversarial training
por: Galashov, Alexandre, et al.
Publicado: (2024)

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
por: Stein, Annika
Publicado: (2023)

A finite sample analysis of the benign overfitting phenomenon for ridge function estimation
por: Caron, Emmanuel, et al.
Publicado: (2020)

Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness
por: Fort, Stanislav, et al.
Publicado: (2024)

A statistical theory of overfitting for imbalanced classification
por: Lyu, Jingyang, et al.
Publicado: (2025)

Efficient local linearity regularization to overcome catastrophic overfitting
por: Rocamora, Elias Abad, et al.
Publicado: (2024)

Robustly overfitting latents for flexible neural image compression
por: Perugachi-Diaz, Yura, et al.
Publicado: (2024)

In-distribution adversarial attacks on object recognition models using gradient-free search
por: Madan, Spandan, et al.
Publicado: (2021)

Robust NAS under adversarial training: benchmark, theory, and beyond
por: Wu, Yongtao, et al.
Publicado: (2024)

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
por: Haas, Moritz, et al.
Publicado: (2023)

Adversarial Defenses via Vector Quantization
por: Dong, Zhiyi, et al.
Publicado: (2023)