Fetching the paper…
Reading the bibliography…
Domain generalization (DG) aims to enhance the ability of models trained on source domains to generalize effectively to unseen domains.
Curvature measures
Herbert Federer · 1959
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate o (1/k2)
Yurii Nesterov · 1983
Earlier work this paper cites.
Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1994
Earlier work this paper cites.
Numerical methods for unconstrained optimization and nonlinear equations
John E Dennis Jr and Robert B Schnabel · 1996
Earlier work this paper cites.
Statistical learning theory
Vladimir Vapnik · 1998
Earlier work this paper cites.
Pac-bayesian model averaging
David A McAllester · 1999
Earlier work this paper cites.
Numerical optimization
Jorge Nocedal and Stephen J Wright · 1999
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma · 2014
Earlier work this paper cites.
Multi-view domain generalization for visual recognition
Li Niu, Wen Li, and Dong Xu · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Earlier work this paper cites.
Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I Loshchilov · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Earlier work this paper cites.
Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Earlier work this paper cites.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Earlier work this paper cites.
Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
Earlier work this paper cites.
Learning from synthetic data: Addressing domain shift for semantic segmentation
Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, and Rama Chellappa · 2018
Earlier work this paper cites.
Adaptive methods for nonconvex optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
A state-of-the-art survey on deep learning theory and architectures
Md Zahangir Alom, Tarek M Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C Van Essen, Abdul AS Awwal, and Vijayan K Asari · 2019
Cited alongside, same era.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Distributionally robust neural networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
Cited alongside, same era.
Multi-adversarial discriminative deep domain generalization for face presentation attack detection
Rui Shao, Xiangyuan Lan, Jiawei Li, and Pong C Yuen · 2019
Cited alongside, same era.
Reducing domain gap by reducing style bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
Later among the works it cites.
Learning explanations that are hard to vary
Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, and Bernhard Schölkopf · 2021
Later among the works it cites.
Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja, and Irina Rish · 2021
Later among the works it cites.
Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
Later among the works it cites.
Adahessian: An adaptive second order optimizer for machine learning
Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, and Michael Mahoney · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Survey on deep neural networks in speech and vision systems
Mahbubul Alam, Manar D Samad, Lasitha Vidyaratne, Alexander Glandon, and Khan M Iftekharuddin · 2020
Cited alongside, same era.
Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang · 2020
Cited alongside, same era.
Ms-net: multi-site network for improving prostate segmentation with heterogeneous mri data
Quande Liu, Qi Dou, Lequan Yu, and Pheng Ann Heng · 2020
Cited alongside, same era.
A survey on modeling and improving reliability of dnn algorithms and accelerators
Sparsh Mittal · 2020
Cited alongside, same era.
Efficient domain generalization via common-specific low-rank decomposition
Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi · 2020
Cited alongside, same era.
Cross-domain face presentation attack detection via multi-domain disentangled representation learning
Guoqing Wang, Hu Han, Shiguang Shan, and Xilin Chen · 2020
Cited alongside, same era.
Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
Cited alongside, same era.
Towards understanding sharpness-aware minimization
Maksym Andriushchenko and Nicolas Flammarion · 2022
Later among the works it cites.
Domain generalization by mutual-information regularization with pre-trained models
Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun · 2022
Later among the works it cites.
When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2022
Later among the works it cites.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
Later among the works it cites.
Towards efficient and scalable sharpness-aware minimization
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You · 2022
Later among the works it cites.
Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2022
Later among the works it cites.
Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and S Yu Philip · 2022
Later among the works it cites.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
Later among the works it cites.
Surrogate gap minimization improves sharpness-aware training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, James s Duncan, Ting Liu, et al · 2022
Later among the works it cites.
Treasure in distribution: a domain randomization based multi-source domain generalization for 2d medical image segmentation
Ziyang Chen, Yongsheng Pan, Yiwen Ye, Hengfei Cui, and Yong Xia · 2023
Later among the works it cites.
On the maximum hessian eigenvalue and generalization
Simran Kaur, Jeremy Cohen, and Zachary Chase Lipton · 2023
Later among the works it cites.
Rethinking domain generalization for face anti-spoofing: Separability and alignment
Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li, and Wen-Sheng Chu · 2023
Later among the works it cites.
Sharpness-aware gradient matching for domain generalization
Pengfei Wang, Zhaoxiang Zhang, Zhen Lei, and Lei Zhang · 2023
Later among the works it cites.
Madg: margin-based adversarial learning for domain generalization
Aveen Dayal, Vimal KB, Linga Reddy Cenkeramaddi, C Mohan, Abhinav Kumar, and Vineeth N Balasubramanian · 2024
Closest in time.
Domain generalization through meta-learning: a survey
Arsham Gholamzadeh Khoee, Yinan Yu, and Robert Feldt · 2024
Closest in time.
Gradient alignment for cross-domain face anti-spoofing
Binh M Le and Simon S Woo · 2024
Closest in time.
Friendly sharpness-aware minimization
Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, and Xiaolin Huang · 2024
Closest in time.
Domain generalization via ensemble stacking for face presentation attack detection
Usman Muhammad, Jorma Laaksonen, Djamila Romaissa Beddiar, and Mourad Oussalah · 2024
Closest in time.
Inter-class and inter-domain semantic augmentation for domain generalization
Mengzhu Wang, Yuehua Liu, Jianlong Yuan, Shanshan Wang, Zhibin Wang, and Wei Wang · 2024
Closest in time.
CR-SAM: Curvature regularized sharpness-aware minimization
Tao Wu, Tie Luo, and Donald C Wunsch II · 2024
Closest in time.