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Deep learning (DL) algorithms have shown significant performance in various computer vision tasks.
One-shot learning of object categories
Li Fei-Fei, Robert Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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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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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 pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2009
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The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Image segmentation with a bounding box prior
Victor Lempitsky, Pushmeet Kohli, Carsten Rother, and Toby Sharp · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, and Pascal Vincent · 2010
Earlier work this paper cites.
Semi-supervised extractive speech summarization via co-training algorithm
Shasha Xie, Hui Lin, and Yang Liu · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Transfer learning for visual categorization: A survey
Ling Shao, Fan Zhu, and Xuelong Li · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Fast r-cnn
Ross Girshick · 2015
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Deep cnn ensemble with data augmentation for object detection
Jian Guo and Stephen Gould · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Audio augmentation for speech recognition
Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
Deepak Pathak, Philipp Krahenbuhl, and Trevor Darrell · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Seed, expand and constrain: Three principles for weakly-supervised image segmentation
Alexander Kolesnikov and Christoph H Lampert · 2016
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A review on image processing and image segmentation
Jiss Kuruvilla, Dhanya Sukumaran, Anjali Sankar, and Siji P Joy · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Stc: A simple to complex framework for weakly-supervised semantic segmentation
Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao, and Shuicheng Yan · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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Understanding data augmentation for classification: when to warp?
Sebastien C Wong, Adam Gatt, Victor Stamatescu, and Mark D McDonnell · 2016
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Discovering class-specific pixels for weakly-supervised semantic segmentation
Arslan Chaudhry, Puneet K Dokania, and Philip HS Torr · 2017
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Dataset augmentation in feature space
Terrance DeVries and Graham W Taylor · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Regional interactive image segmentation networks
JunHao Liew, Yunchao Wei, Wei Xiong, Sim-Heng Ong, and Jiashi Feng · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Data augmentation for plant classification
Pornntiwa Pawara, Emmanuel Okafor, Lambert Schomaker, and Marco Wiering · 2017
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The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
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Semi supervised semantic segmentation using generative adversarial network
Nasim Souly, Concetto Spampinato, and Mubarak Shah · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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A-fast-rcnn: Hard positive generation via adversary for object detection
Xiaolong Wang, Abhinav Shrivastava, and Abhinav Gupta · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
Jiwoon Ahn and Suha Kwak · 2018
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Improving consistency-based semi-supervised learning with weight averaging
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Semi-supervised deep learning with memory
Yanbei Chen, Xiatian Zhu, and Shaogang Gong · 2018
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Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
Earlier work this paper cites.
Weakly-supervised semantic segmentation network with deep seeded region growing
Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, and Jingdong Wang · 2018
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
Smooth neighbors on teacher graphs for semi-supervised learning
Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, and Bo Zhang · 2018
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Deep co-training for semi-supervised image recognition
Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, and Alan Yuille · 2018
Cited alongside, same era.
Semi-supervised semantic segmentation with cross-consistency training
Yassine Ouali, Céline Hudelot, and Myriam Tami · 2020
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Search of an optimal sound augmentation policy for environmental sound classification with deep neural networks
Jinbae Park, Teerath Kumar, and Sung-Ho Bae · 2020
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Resizemix: Mixing data with preserved object information and true labels
Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, and Xinggang Wang · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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A survey on data augmentation methods based on gan in computer vision
Xingzhe Su · 2020
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An analysis of scale invariance in object detection snip
Bharat Singh and Larry S Davis · 2018
Cited alongside, same era.
Sniper: Efficient multi-scale training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
Cited alongside, same era.
Hide-and-seek: A data augmentation technique for weakly-supervised localization and beyond
Krishna Kumar Singh, Hao Yu, Aron Sarmasi, Gautam Pradeep, and Yong Jae Lee · 2018
Cited alongside, same era.
Ricap: Random image cropping and patching data augmentation for deep cnns
Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara · 2018
Cited alongside, same era.
Adversarial feature augmentation for unsupervised domain adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese, and Vittorio Murino · 2018
Cited alongside, same era.
Weakly-supervised semantic segmentation by iteratively mining common object features
Xiang Wang, Shaodi You, Xi Li, and Huimin Ma · 2018
Cited alongside, same era.
Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation
Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang · 2018
Cited alongside, same era.
Saliencymix: A saliency guided data augmentation strategy for better regularization
AFM Uddin, Mst Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae, et al · 2020
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A survey on face data augmentation for the training of deep neural networks
Xiang Wang, Kai Wang, and Shiguo Lian · 2020
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Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation
Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen · 2020
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Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy
Jaejun Yoo, Namhyuk Ahn, and Kyung-Ah Sohn · 2020
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Deep adversarial data augmentation for extremely low data regimes
Xiaofeng Zhang, Zhangyang Wang, Dong Liu, Qifeng Lin, and Qing Ling · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V Le · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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Pseudoseg: Designing pseudo labels for semantic segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister · 2020
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A survey on data augmentation for text classification
Markus Bayer, Marc-André Kaufhold, and Christian Reuter · 2021
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Audd: audio urdu digits dataset for automatic audio urdu digit recognition
Aisha Chandio, Yao Shen, Malika Bendechache, Irum Inayat, and Teerath Kumar · 2021
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Robust and accurate object detection via adversarial learning
Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, and Boqing Gong · 2021
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Scale-aware automatic augmentation for object detection
Yukang Chen, Yanwei Li, Tao Kong, Lu Qi, Ruihang Chu, Lei Li, and Jiaya Jia · 2021
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Salfmix: A novel single image-based data augmentation technique using a saliency map
Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, and Heechul Jung · 2021
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Simple copy-paste is a strong data augmentation method for instance segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
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Keepaugment: A simple information-preserving data augmentation approach
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, and Qiang Liu · 2021
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Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
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Neural style transfer as data augmentation for improving covid-19 diagnosis classification
Netzahualcoyotl Hernandez-Cruz, David Cato, and Jesus Favela · 2021
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Stylemix: Separating content and style for enhanced data augmentation
Minui Hong, Jinwoo Choi, and Gunhee Kim · 2021
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Snapmix: Semantically proportional mixing for augmenting fine-grained data
Shaoli Huang, Xinchao Wang, and Dacheng Tao · 2021
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Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning
Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon, Seongho Joe, Seungjai Min, and Youngjune Gwon · 2021
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Local augment: Utilizing local bias property of convolutional neural networks for data augmentation
Youmin Kim, AFM Shahab Uddin, and Sung-Ho Bae · 2021
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Class specific autoencoders enhance sample diversity
Teerath Kumar, Jinbae Park, Muhammad Salman Ali, AFM Uddin, and Sung-Ho Bae · 2021
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Binary-classifiers-enabled filters for semi-supervised learning
Teerath Kumar, Jinbae Park, Muhammad Salman Ali, AFM Shahab Uddin, Jong Hwan Ko, and Sung-Ho Bae · 2021
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Local patch autoaugment with multi-agent collaboration
Shiqi Lin, Tao Yu, Ruoyu Feng, Xin Li, Xin Jin, and Zhibo Chen · 2021
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Classmix: Segmentation-based data augmentation for semi-supervised learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, and Lennart Svensson · 2021
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Mixmo: Mixing multiple inputs for multiple outputs via deep subnetworks
Alexandre Ramé, Rémy Sun, and Matthieu Cord · 2021
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Self-augmentation: Generalizing deep networks to unseen classes for few-shot learning
Jin-Woo Seo, Hong-Gyu Jung, and Seong-Whan Lee · 2021
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Text data augmentation for deep learning
Connor Shorten, Taghi M Khoshgoftaar, and Borko Furht · 2021
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Context decoupling augmentation for weakly supervised semantic segmentation
Yukun Su, Ruizhou Sun, Guosheng Lin, and Qingyao Wu · 2021
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Cut-thumbnail: A novel data augmentation for convolutional neural network
Tianshu Xie, Xuan Cheng, Xiaomin Wang, Minghui Liu, Jiali Deng, Tao Zhou, and Ming Liu · 2021
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A simple baseline for semi-supervised semantic segmentation with strong data augmentation
Jianlong Yuan, Yifan Liu, Chunhua Shen, Zhibin Wang, and Hao Li · 2021
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Objectaug: object-level data augmentation for semantic image segmentation
Jiawei Zhang, Yanchun Zhang, and Xiaowei Xu · 2021
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Random data augmentation based enhancement: Ageneralized enhancement approach for medical datasets
Sidra Aleem, Teerath Kumar, Suzanne Little, Malika Bendechache, Rob Brennan, and Kevin McGuinness · 2022
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Investigation of effectiveness of shuffled frog-leaping optimizer in training a convolution neural network
Soroush Baseri Saadi, Nazanin Tataei Sarshar, Soroush Sadeghi, Ramin Ranjbarzadeh, Mersedeh Kooshki Forooshani, and Malika Bendechache · 2022
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Aisha Chandio, Gong Gui, Teerath Kumar, Irfan Ullah, Ramin Ranjbarzadeh, Arunabha M Roy, Akhtar Hussain, and Yao Shen · 2022
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You only cut once: Boosting data augmentation with a single cut
Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, and Hongdong Li · 2022
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Introducing urdu digits dataset with demonstration of an efficient and robust noisy decoder-based pseudo example generator
Wisal Khan, Kislay Raj, Teerath Kumar, Arunabha M Roy, and Bin Luo · 2022
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Randommix: A mixed sample data augmentation method with multiple mixed modes
Xiaoliang Liu, Furao Shen, Jian Zhao, and Changhai Nie · 2022
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Rangeaugment: Efficient online augmentation with range learning
Sachin Mehta, Saeid Naderiparizi, Fartash Faghri, Maxwell Horton, Lailin Chen, Ali Farhadi, Oncel Tuzel, and Mohammad Rastegari · 2022
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Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods
Ramin Ranjbarzadeh, Shadi Dorosti, Saeid Jafarzadeh Ghoushchi, Annalina Caputo, Erfan Babaee Tirkolaee, Sadia Samar Ali, Zahra Arshadi, and Malika Bendechache · 2022
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Mrfe-cnn: multi-route feature extraction model for breast tumor segmentation in mammograms using a convolutional neural network
Ramin Ranjbarzadeh, Nazanin Tataei Sarshar, Saeid Jafarzadeh Ghoushchi, Mohammad Saleh Esfahani, Mahboub Parhizkar, Yaghoub Pourasad, Shokofeh Anari, and Malika Bendechache · 2022
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Brain tumor segmentation based on an optimized convolutional neural network and an improved chimp optimization algorithm
Ramin Ranjbarzadeh, Payam Zarbakhsh, Annalina Caputo, Erfan Babaee Tirkolaee, and Malika Bendechache · 2022
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Wildect-yolo: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection
Arunabha M Roy, Jayabrata Bhaduri, Teerath Kumar, and Kislay Raj · 2022
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Glioma brain tumor segmentation in four mri modalities using a convolutional neural network and based on a transfer learning method
Nazanin Tataei Sarshar, Ramin Ranjbarzadeh, Saeid Jafarzadeh Ghoushchi, Gabriel Gomes de Oliveira, Shokofeh Anari, Mahboub Parhizkar, and Malika Bendechache · 2022
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Investigating multi-feature selection and ensembling for audio classification
Muhammad Turab, Teerath Kumar, Malika Bendechache, and Takfarinas Saber · 2022
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A comprehensive survey of image augmentation techniques for deep learning
Mingle Xu, Sook Yoon, Alvaro Fuentes, and Dong Sun Park · 2022
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Image data augmentation for deep learning: A survey
Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, and Furao Shen · 2022
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Survey of image augmentation based on generative adversarial network
Fei Yue, Chao Zhang, MingYang Yuan, Chen Xu, and YaLin Song · 2022
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Rsmda: Random slices mixing data augmentation
Teerath Kumar, Alessandra Mileo, Rob Brennan, and Malika Bendechache · 2023
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Me-ccnn: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition
Ramin Ranjbarzadeh, Saeid Jafarzadeh Ghoushchi, Nazanin Tataei Sarshar, Erfan Babaee Tirkolaee, Sadia Samar Ali, Teerath Kumar, and Malika Bendechache · 2023
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Understanding eeg signals for subject-wise definition of armoni activities
Aditya Singh, Ramin Ranjbarzadeh, Kislay Raj, Teerath Kumar, and Arunabha M Roy · 2023
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