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Data augmentation is crucial in training deep models, preventing them from overfitting to limited data.
Contrastive examples for addressing the tyranny of the majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell, and Novi Quadrianto · 2004
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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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The caltech-ucsd birds-200-2011 dataset, 2011
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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3D object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi · 2013
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Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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What can help pedestrian detection?, 2017
Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan · 2018
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Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2018
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Auggan: Cross domain adaptation with gan-based data augmentation
Sheng-Wei Huang, Che-Tsung Lin, Shu-Ping Chen, Yen-Yi Wu an Po-Hao Hsu, and Shang-Hong Lai · 2018
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Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan Yuille · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
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Generate to adapt: Aligning domains using generative adversarial networks
Swami Sankaranarayanan, Yogesh Balaji, Carlos Domingo Castillo, and Rama Chellappa · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, and Timothy M. Hospedales · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Associative alignment for few-shot image classification
Arman Afrasiyabi, Jean-François Lalonde, and Christian Gagné · 2019
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Antreas Antoniou and Amos Storkey · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Image deformation meta-networks for one-shot learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Diversity with cooperation: Ensemble methods for few-shot classification
Nikita Dvornik, Julien Mairal, and Cordelia Schmid · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Unsupervised meta-learning for few-shot image classification
Siavash Khodadadeh, Ladislau Boloni, and Mubarak Shah · 2019
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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X. Yu · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 2020
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AugMix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Cited alongside, same era.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
Cited alongside, same era.
Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
Cited alongside, same era.
Adversarial feature hallucination networks for few-shot learning
Kai Li, Yulun Zhang, Kunpeng Li, and Yun Fu · 2020
Cited alongside, same era.
Negative margin matters: Understanding margin in few-shot classification
Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu · 2020
Cited alongside, same era.
Self-supervised prototypical transfer learning for few-shot classification
Automix: Unveiling the power of mixup for stronger classifiers
Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, and Stan Z Li · 2022
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Self-supervision can be a good few-shot learner
Yuning Lu, Liangjian Wen, Jianzhuang Liu, Yajing Liu, and Xinmei Tian · 2022
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Boomerang: Local sampling on image manifolds using diffusion models, 2022
Lorenzo Luzi, Ali Siahkoohi, Paul M Mayer, Josue Casco-Rodriguez, and Richard Baraniuk · 2022
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Gan-supervised dense visual alignment
William Peebles, Jun-Yan Zhu, Richard Zhang, Antonio Torralba, Alexei Efros, and Eli Shechtman · 2022
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On guiding visual attention with language specification
Suzanne Petryk, Lisa Dunlap, Keyan Nasseri, Joseph Gonzalez, Trevor Darrell, and Anna Rohrbach · 2022
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Carlos Medina, Arnout Devos, and Matthias Grossglauser · 2020
Cited alongside, same era.
Unsupervised few-shot learning via distribution shift-based augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi, and Gao Yang · 2020
Cited alongside, same era.
When does self-supervision improve few-shot learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 2020
Cited alongside, same era.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
Cited alongside, same era.
Few-shot learning via embedding adaptation with set-to-set functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
Cited alongside, same era.
Ace: Ally complementary experts for solving long-tailed recognition in one-shot
Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang, et al · 2021
Cited alongside, same era.
Few-shot learning with part discovery and augmentation from unlabeled images
Wentao Chen, Chenyang Si, Wei Wang, Liang Wang, Zilei Wang, and Tieniu Tan · 2021
Cited alongside, same era.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Vl-ltr: Learning class-wise visual-linguistic representation for long-tailed visual recognition
Changyao Tian, Wenhai Wang, Xizhou Zhu, Jifeng Dai, and Yu Qiao · 2022
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Deit iii: Revenge of the vit
Hugo Touvron, Matthieu Cord, and Hervé Jégou · 2022
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Contrastive prototypical network with wasserstein confidence penalty
Haoqing Wang and Zhi-Hong Deng · 2022
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Towards calibrated hyper-sphere representation via distribution overlap coefficient for long-tailed learning
Hualiang Wang, Siming Fu, Xiaoxuan He, Hangxiang Fang, Zuozhu Liu, and Haoji Hu · 2022
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Constructing balance from imbalance for long-tailed image recognition
Yue Xu, Yong-Lu Li, Jiefeng Li, and Cewu Lu · 2022
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Balanced contrastive learning for long-tailed visual recognition
Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang · 2022
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Synthetic data from diffusion models improves imagenet classification, 2023
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J. Fleet · 2023
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Identifying and mitigating model failures through few-shot clip-aided diffusion generation
Atoosa Chegini and Soheil Feizi · 2023
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Diverse data augmentation with diffusions for effective test-time prompt tuning, 2023
Chun-Mei Feng, Kai Yu, Yong Liu, Salman Khan, and Wangmeng Zuo · 2023
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Feedback-guided data synthesis for imbalanced classification, 2023
Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, and Adriana Romero-Soriano · 2023
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Meta-dm: Applications of diffusion models on few-shot learning, 2023
Wentao Hu, Xiurong Jiang, Jiarun Liu, Yuqi Yang, and Hui Tian · 2023
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Your diffusion model is secretly a zero-shot classifier, 2023
Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
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Instaflow: One step is enough for high-quality diffusion-based text-to-image generation
Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, et al · 2023
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Camdiff: Camouflage image augmentation via diffusion model, 2023
Xue-Jing Luo, Shuo Wang, Zongwei Wu, Christos Sakaridis, Yun Cheng, Deng-Ping Fan, and Luc Van Gool · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
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Cap2aug: Caption guided image to image data augmentation, 2023
Aniket Roy, Anshul Shah, Ketul Shah, Anirban Roy, and Rama Chellappa · 2023
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Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
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Boosting zero-shot classification with synthetic data diversity via stable diffusion
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes · 2023
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Self-attention message passing for contrastive few-shot learning
Ojas Kishorkumar Shirekar, Anuj Singh, and Hadi Jamali-Rad · 2023
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Transductive decoupled variational inference for few-shot classification
Anuj Rajeeva Singh and Hadi Jamali-Rad · 2023
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Learning imbalanced data with vision transformers, 2023
Zhengzhuo Xu, Ruikang Liu, Shuo Yang, Zenghao Chai, and Chun Yuan · 2023
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SAFLEX: Self-adaptive augmentation via feature label extrapolation
Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, and Furong Huang · 2024
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Lance: Stress-testing visual models by generating language-guided counterfactual images
Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay, and Judy Hoffman · 2024
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Effective data augmentation with diffusion models
Brandon Trabucco, Kyle Doherty, Max A Gurinas, and Ruslan Salakhutdinov · 2024
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