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Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks.
An experimental study on pedestrian classification
Stefan Munder and Dariu M Gavrila · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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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 caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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How do humans sketch objects?
Mathias Eitz, James Hays, and Marc Alexa · 2012
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How do humans sketch objects?
Mathias Eitz, James Hays, and Marc Alexa · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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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
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Babak Saleh and A. Elgammal · 2015
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Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Babak Saleh and Ahmed Elgammal · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Adding new tasks to a single network with weight transformations using binary masks
Massimilano Mancini, Elisa Ricci, Barbara Caputo, and Samuel Rota Bulò · 2018
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Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
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Incremental learning through deep adaptation
Amir Rosenfeld and John K Tsotsos · 2018
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Budget-aware adapters for multi-domain learning
Rodrigo Berriel, Stephane Lathuillere, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe, and Elisa Ricci · 2019
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Cited alongside, same era.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Iasonas Kokkinos · 2017
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Cited alongside, same era.
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris · 2017
Cited alongside, same era.
Yuan Gao, Jiayi Ma, Mingbo Zhao, Wei Liu, and Alan L Yuille · 2019
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Spottune: transfer learning through adaptive fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
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Nettailor: Tuning the architecture, not just the weights
Pedro Morgado and Nuno Vasconcelos · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Latent multi-task architecture learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard · 2019
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Branched multi-task networks: deciding what layers to share
Simon Vandenhende, Stamatios Georgoulis, Bert De Brabandere, and Luc Van Gool · 2019
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Discovering neural wirings
Mitchell Wortsman, Ali Farhadi, and Mohammad Rastegari · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Adashare: Learning what to share for efficient deep multi-task learning
Ximeng Sun, Rameswar Panda, Rogerio Feris, and Kate Saenko · 2020
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Side-tuning: A baseline for network adaptation via additive side networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Malik · 2020
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How to train your vit? data, augmentation, and regularization in vision transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2021
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