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Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth.
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Deep learning
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You only look once: Unified, real-time object detection
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Sebastian Ruder · 2016
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Terrance DeVries and Graham W Taylor · 2017
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Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Mingxing Tan and Quoc Le · 2019
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Implicit semantic data augmentation for deep networks
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Multimodal image translation with stochastic style representations and mutual information loss
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Cognitive psychology for deep neural networks: A shape bias case study
Samuel Ritter, David GT Barrett, Adam Santoro, and Matt M Botvinick · 2017
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Gan augmentation: Augmenting training data using generative adversarial networks
Christopher Bowles, Liang Chen, Ricardo Guerrero, et al · 2018
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Dropblock: a regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
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High-resolution image synthesis and semantic manipulation with conditional gans
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
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Sanghyeon Na, Seungjoo Yoo, and Jaegul Choo · 2020
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Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
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Light-weight calibrator: a separable component for unsupervised domain adaptation
Shaokai Ye, Kailu Wu, Mu Zhou, et al · 2020
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Filter style transfer between photos
Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, and Jihwan Choe · 2020
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Repaint: Improving the generalization of down-stream visual tasks by generating multiple instances of training examples
Amin Banitalebi-Dehkordi and Yong Zhang · 2021
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Classmix: Segmentation-based data augmentation for semi-supervised learning
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