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Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside.
“Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories”
Li Fei-Fei, Rob Fergus and Pietro Perona · 2004
Earlier work this paper cites.
“Caltech-256 object category dataset”
Gregory Griffin, Alex Holub and Pietro Perona · 2007
Earlier work this paper cites.
“Automated flower classification over a large number of classes”
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
Earlier work this paper cites.
“Learning Multiple Layers of Features from Tiny Images”
Alex Krizhevsky · 2009
Earlier work this paper cites.
“Sun database: Large-scale scene recognition from abbey to zoo”
Jianxiong Xiao, James Hays, Krista Ehinger, Aude Oliva and Antonio Torralba · 2010
Earlier work this paper cites.
“Poisoning attacks against support vector machines”
Battista Biggio, Blaine Nelson and Pavel Laskov · 2012
Earlier work this paper cites.
“Cats and dogs”
Omkar Parkhi, Andrea Vedaldi, Andrew Zisserman and CV Jawahar · 2012
Earlier work this paper cites.
“Adversarial Label Flips Attack on Support Vector Machines.”
Han Xiao, Huang Xiao and Claudia Eckert · 2012
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“Collecting a large-scale dataset of fine-grained cars”, 2013
Jonathan Krause, Jia Deng, Michael Stark and Li Fei-Fei · 2013
Earlier work this paper cites.
“Fine-grained visual classification of aircraft”
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko and Andrea Vedaldi · 2013
Earlier work this paper cites.
“Food-101–mining discriminative components with random forests”
Lukas Bossard, Matthieu Guillaumin and Luc Van · 2014
Earlier work this paper cites.
“Birdsnap: Large-scale fine-grained visual categorization of birds”
Thomas Berg, Jiongxin Liu, Seung Woo, Michelle Alexander, David Jacobs and Peter Belhumeur · 2014
Earlier work this paper cites.
“Rich feature hierarchies for accurate object detection and semantic segmentation”
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik · 2014
Earlier work this paper cites.
“Microsoft coco: Common objects in context”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár and C Zitnick · 2014
Earlier work this paper cites.
“On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis”
Andrew Newell, Rahul Potharaju, Luojie Xiang and Cristina Nita-Rotaru · 2014
Earlier work this paper cites.
“Deep Learning Face Attributes in the Wild”
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang · 2015
Earlier work this paper cites.
“Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners.”
Shike Mei and Xiaojin Zhu · 2015
Cited alongside, same era.
“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, Alexander. Berg and Li Fei-Fei · 2015
Cited alongside, same era.
“Faster r-cnn: Towards real-time object detection with region proposal networks”
Shaoqing Ren, Kaiming He, Ross Girshick and Jian Sun · 2015
Cited alongside, same era.
“R-fcn: Object detection via region-based fully convolutional networks”
Jifeng Dai, Yi Li, Kaiming He and Jian Sun · 2016
Cited alongside, same era.
“Transfer learning from deep features for remote sensing and poverty mapping”
Michael Xie, Neal Jean, Marshall Burke, David Lobell and Stefano Ermon · 2016
Cited alongside, same era.
“ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models”
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum and Boris Katz · 2019
Later among the works it cites.
“Self-driving car steering angle prediction based on image recognition”
Shuyang Du, Haoli Guo and Andrew Simpson · 2019
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“ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.”
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix. Wichmann and Wieland Brendel · 2019
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“Do better imagenet models transfer better?”
Simon Kornblith, Jonathon Shlens and Quoc Le · 2019
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“Label-Consistent Backdoor Attacks”, 2019
Alexander Turner, Dimitris Tsipras and Aleksander Madry · 2019
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“Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs”
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy and Alan Yuille · 2017
Cited alongside, same era.
“Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain”
Tianyu Gu, Brendan Dolan-Gavitt and Siddharth Garg · 2017
Cited alongside, same era.
“End-to-end ego lane estimation based on sequential transfer learning for self-driving cars”
Jiman Kim and Chanjong Park · 2017
Cited alongside, same era.
“Certified Defenses for Data Poisoning Attacks”
Jacob Steinhardt, Pang Koh and Percy Liang · 2017
Cited alongside, same era.
“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
Cited alongside, same era.
“Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases”
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri and Ronald Summers · 2017
Cited alongside, same era.
“Object Recognition without and without Objects”
Zhuotun Zhu, Lingxi Xie and Alan Yuille · 2017
Cited alongside, same era.
“Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques”
Sherrie Wang, George Azzari and David Lobell · 2019
Later among the works it cites.
“Language models are few-shot learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry and Amanda Askell · 2020
Later among the works it cites.
“Do Adversarially Robust ImageNet Models Transfer Better?”
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor and Aleksander Madry · 2020
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“Don’t judge an object by its context: learning to overcome contextual bias”
Krishna Singh, Dhruv Mahajan, Kristen Grauman, Yong Lee, Matt Feiszli and Deepti Ghadiyaram · 2020
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“Adversarially-Trained Deep Nets Transfer Better”
Francisco Utrera, Evan Kravitz, N. Erichson, Rajiv Khanna and Michael. Mahoney · 2020
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“Noise or signal: The role of image backgrounds in object recognition”
Kai Xiao, Logan Engstrom, Andrew Ilyas and Aleksander Madry · 2020
Later among the works it cites.
“CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation”
Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Ng and Pranav Rajpurkar · 2021
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“3DB: A Framework for Debugging Computer Vision Models”
Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar and Greg Yang · 2021
Later among the works it cites.
“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
Later among the works it cites.
“A comprehensive study on face recognition biases beyond demographics”
Philipp Terhörst, Jan Kolf, Marco Huber, Florian Kirchbuchner, Naser Damer, Aythami Morales, Julian Fierrez and Arjan Kuijper · 2021
Later among the works it cites.
“ffcv”, https://github.com/libffcv/ffcv/ , 2022
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Park, Hadi Salman and Aleksander Madry · 2022
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