Learning Soft Labels via Meta Learning

Learning multiple layers of features from tiny images

Alex Krizhevsky · 2009

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012

Earlier work this paper cites.

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

Earlier work this paper cites.

Distilling the knowledge in a neural network

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015

Earlier work this paper cites.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

Sergey Ioffe and Christian Szegedy · 2015

Earlier work this paper cites.

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

Earlier work this paper cites.

Convolutional neural fabrics

Shreyas Saxena and Jakob Verbeek · 2016

Earlier work this paper cites.

Rethinking the inception architecture for computer vision

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016

Earlier work this paper cites.

Disturblabel: Regularizing cnn on the loss layer

Lingxi Xie, Jingdong Wang, Zhen Wei, Meng Wang, and Qi Tian · 2016

Earlier work this paper cites.

Wide residual networks

Sergey Zagoruyko and Nikos Komodakis · 2016

Earlier work this paper cites.

Towards better decoding and language model integration in sequence to sequence models

Jan Chorowski and Navdeep Jaitly · 2017

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Improving machine learning ability with fine-tuning

John P. Lalor, Hao Wu, and Hong Yu · 2017

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Focal loss for dense object detection

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017

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Regularizing neural networks by penalizing confident output distributions

Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017

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Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017

Cited alongside, same era.

Co-teaching: Robust training of deep neural networks with extremely noisy labels

Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018

Label-similarity curriculum learning

Urun Dogan, Aniket Anand Deshmukh, Marcin Machura, and Christian Igel · 2019

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Learning to impute: A general framework for semi-supervised learning

Wei-Hong Li, Chuan-Sheng Foo, and Hakan Bilen · 2019

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When does label smoothing help?

Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton · 2019

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Regularized evolution for image classifier architecture search

Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019

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Data parameters: A new family of parameters for learning a differentiable curriculum

Shreyas Saxena, Oncel Tuzel, and Dennis DeCoste · 2019

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Meta-weight-net: Learning an explicit mapping for sample weighting

Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019