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Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.
“Random Search and Reproducibility for Neural Architecture Search”, 2019
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“Imagenet: A large-scale hierarchical image database”
Jia Deng et al · 2009
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“Learning multiple layers of features from tiny images”
Alex Krizhevsky and Geoffrey Hinton · 2009
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“Super-samples from kernel herding”
Yutian Chen, Max Welling and Alex Smola · 2010
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“icarl: Incremental classifier and representation learning”
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl and Christoph Lampert · 2010
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“Reading digits in natural images with unsupervised feature learning”, 2011
Yuval Netzer et al · 2011
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“Facility location: concepts, models, algorithms and case studies. Series: Contributions to Management Science”
Gert Wolf · 2011
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“Random search for hyper-parameter optimization.”
James Bergstra and Yoshua Bengio · 2012
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
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“Unsupervised representation learning with deep convolutional generative adversarial networks”
Alec Radford, Luke Metz and Soumith Chintala · 2015
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“Going deeper with convolutions”
Christian Szegedy et al · 2015
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“Deep learning”
Ian Goodfellow, Yoshua Bengio, Aaron Courville and Yoshua Bengio · 2016
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Cited alongside, same era.
“Improved regularization of convolutional neural networks with cutout”
Terrance DeVries and Graham Taylor · 2017
Cited alongside, same era.
“Neural architecture search: A survey”
Thomas Elsken, Jan Metzen and Frank Hutter · 2017
Cited alongside, same era.
“Active learning for convolutional neural networks: A core-set approach”
Ozan Sener and Silvio Savarese · 2017
Cited alongside, same era.
“Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis”
Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky · 2017
Cited alongside, same era.
“Nas-bench-101: Towards reproducible neural architecture search”
Chris Ying et al · 2019
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“Randaugment: Practical automated data augmentation with a reduced search space”
Ekin Cubuk, Barret Zoph, Jonathon Shlens and Quoc Le · 2020
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“Nas-bench-201: Extending the scope of reproducible neural architecture search”
Xuanyi Dong and Yi Yang · 2020
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“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
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“Dataset Meta-Learning from Kernel Ridge-Regression”
Timothy Nguyen, Zhourong Chen and Jaehoon Lee · 2020
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Ashish Vaswani et al · 2017
Cited alongside, same era.
“Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
Cited alongside, same era.
“Neural Architecture Search with Reinforcement Learning”
Barret Zoph and Quoc. Le · 2017
Cited alongside, same era.
“Understanding and Simplifying One-Shot Architecture Search”
Gabriel Bender et al · 2018
Cited alongside, same era.
“End-to-end incremental learning”
Francisco Castro et al · 2018
Cited alongside, same era.
“Autoaugment: Learning augmentation policies from data”
Ekin Cubuk et al · 2018
Cited alongside, same era.
“Data augmentation by pairing samples for images classification”
Hiroshi Inoue · 2018
Cited alongside, same era.
Ngoc-Trung Tran et al · 2020
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“Efficient Neural Architecture Search via Proximal Iterations”
Quanming Yao, Ju Xu, Wei-Wei Tu and Zhanxing Zhu · 2020
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“Dataset Condensation with Gradient Matching”
Bo Zhao, Konda Mopuri and Hakan Bilen · 2020
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“Differentiable augmentation for data-efficient gan training”
Shengyu Zhao et al · 2020
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“DrNAS: Dirichlet Neural Architecture Search”
Xiangning Chen et al · 2021
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“Generalizing Few-Shot NAS with Gradient Matching”
Shoukang Hu et al · 2021
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“Dataset Distillation with Infinitely Wide Convolutional Networks”
Timothy Nguyen, Roman Novak, Lechao Xiao and Jaehoon Lee · 2021
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“RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving”
Ruochen Wang et al · 2021
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“Rethinking Architecture Selection in Differentiable NAS”
Ruochen Wang et al · 2021
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“Dataset condensation with differentiable siamese augmentation”
Bo Zhao and Hakan Bilen · 2021
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“Dataset Condensation with Distribution Matching”
Bo Zhao and Hakan Bilen · 2021
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“Dataset distillation by matching training trajectories”
George Cazenavette et al · 2022
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“Bidirectional learning for offline infinite-width model-based optimization”
Can Chen et al · 2022
Closest in time.