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Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts.
A method for solving the convex programming problem with convergence rate
Yu E Nesterov · 1983
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Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1994
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Some large-scale matrix computation problems
Zhaojun Bai, Gark Fahey, and Gene Golub · 1996
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An overview of statistical learning theory
Vladimir N Vapnik · 1999
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
Haim Avron and Sivan Toledo · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A Efros, and Antonio Torralba · 2012
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Domain generalization by marginal transfer learning, 2017
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott · 2017
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Three factors influencing minima in sgd
Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey · 2017
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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 · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Learning to generalize: Meta-learning for domain generalization, 2017
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
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Neon2: Finding local minima via first-order oracles
Zeyuan Allen-Zhu and Yuanzhi Li · 2018
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Closing the generalization gap of adaptive gradient methods in training deep neural networks
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, and Quanquan Gu · 2018
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Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot · 2018
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Domain generalization via conditional invariant representation, 2018
Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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Best sources forward: domain generalization through source-specific nets
Massimiliano Mancini, Samuel Rota Bulo, Barbara Caputo, and Elisa Ricci · 2018
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Robust place categorization with deep domain generalization
Massimiliano Mancini, Samuel Rota Bulo, Barbara Caputo, and Elisa Ricci · 2018
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Generalizing across domains via cross-gradient training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 2018
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A bayesian perspective on generalization and stochastic gradient descent
Samuel L. Smith and Quoc V. Le · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
First-order stochastic algorithms for escaping from saddle points in almost linear time
Yi Xu, Rong Jin, and Tianbao Yang · 2018
Cited alongside, same era.
Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 2018
Cited alongside, same era.
Adaptive methods for nonconvex optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Gradient descent on neural networks typically occurs at the edge of stability
Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, and Ameet Talwalkar · 2021
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Efficient sharpness-aware minimization for improved training of neural networks
Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, and Vincent YF Tan · 2021
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
Cited alongside, same era.
Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M Hospedales · 2019
Cited alongside, same era.
Adaptive gradient methods with dynamic bound of learning rate
Liangchen Luo, Yuanhao Xiong, Yan Liu, and Xu Sun · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi · 2021
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Reducing domain gap by reducing style bias
Hyeonseob Nam, Hyunjae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
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Permuted adain: Reducing the bias towards global statistics in image classification
O. Nuriel, S. Benaim, and L. Wolf · 2021
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Relative flatness and generalization
Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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A fourier-based framework for domain generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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Why stable learning works? a theory of covariate shift generalization
Renzhe Xu, Peng Cui, Zheyan Shen, Xingxuan Zhang, and Tong Zhang · 2021
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Adahessian: An adaptive second order optimizer for machine learning
Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, and Michael Mahoney · 2021
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Adaptive risk minimization: A meta-learning approach for tackling group shift, 2021
Marvin Mengxin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2021
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Deep stable learning for out-of-distribution generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, and Zheyan Shen · 2021
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Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
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Domain generalization by mutual-information regularization with pre-trained models
Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun · 2022
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Dna: Domain generalization with diversified neural averaging
Xu Chu, Yujie Jin, Wenwu Zhu, Yasha Wang, Xin Wang, Shanghang Zhang, and Hong Mei · 2022
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Sharpness-aware training for free
Jiawei Du, Daquan Zhou, Jiashi Feng, Vincent YF Tan, and Joey Tianyi Zhou · 2022
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On the maximum hessian eigenvalue and generalization
Simran Kaur, Jeremy Cohen, and Zachary C Lipton · 2022
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Fisher sam: Information geometry and sharpness aware minimisation
Minyoung Kim, Da Li, Shell X Hu, and Timothy Hospedales · 2022
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Sharpness-aware minimization for worst case optimization
Taero Kim, Sungjun Lim, and Kyungwoo Song · 2022
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Towards efficient and scalable sharpness-aware minimization
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You · 2022
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Context-aware robust fine-tuning
Xiaofeng Mao, Yuefeng Chen, Xiaojun Jia, Rong Zhang, Hui Xue, and Zhao Li · 2022
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Make sharpness-aware minimization stronger: A sparsified perturbation approach
Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, and Dacheng Tao · 2022
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Empirical study on optimizer selection for out-of-distribution generalization
Hiroki Naganuma, Kartik Ahuja, Ioannis Mitliagkas, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, and Ikuro Sato · 2022
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Recycling diverse models for out-of-distribution generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, and David Lopez-Paz · 2022
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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2022
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Diverse weight averaging for out-of-distribution generalization
Alexandre Rame, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, and Matthieu Cord · 2022
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A closer look at smoothness in domain adversarial training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, and Venkatesh Babu Radhakrishnan · 2022
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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 Philip Yu · 2022
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Causal balancing for domain generalization
Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, and William Yang Wang · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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On the power-law spectrum in deep learning: A bridge to protein science
Zeke Xie, Qian-Yuan Tang, Yunfeng Cai, Mingming Sun, and Ping Li · 2022
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Adaptive inertia: Disentangling the effects of adaptive learning rate and momentum
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, and Masashi Sugiyama · 2022
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A theoretical analysis on independence-driven importance weighting for covariate-shift generalization
Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, and Peng Cui · 2022
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Towards principled disentanglement for domain generalization
Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, and Eric P Xing · 2022
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Nico++: Towards better benchmarking for domain generalization
Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, and Haoxin Liu · 2022
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Penalizing gradient norm for efficiently improving generalization in deep learning
Yang Zhao, Hao Zhang, and Xiuyuan Hu · 2022
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Improving sharpness-aware minimization with fisher mask for better generalization on language models
Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, and Dacheng Tao · 2022
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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
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Simple: Specialized model-sample matching for domain generalization
Ziyue Li, Kan Ren, XINYANG JIANG, Yifei Shen, Haipeng Zhang, and Dongsheng Li · 2023
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Gradient norm aware minimization seeks first-order flatness and improves generalization
Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, and Peng Cui · 2023
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Free lunch for domain adversarial training: Environment label smoothing
YiFan Zhang, Xue Wang, Jian Liang, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan · 2023
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