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Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement.
Learning long-term dependencies with gradient descent is difficult
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Fast and flexible convolutional sparse coding
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Deep metric learning using triplet network
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Very deep convolutional networks for large-scale image recognition
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On orthogonality and learning recurrent networks with long term dependencies
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All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation
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Learning multi-attention convolutional neural network for fine-grained image recognition
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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All you need is a good init
D. Mishkin and J. Matas · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
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Full-capacity unitary recurrent neural networks
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A spline theory of deep networks
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Can we gain more from orthogonality regularizations in training deep cnns?
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Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks
L. Huang, X. Liu, B. Lang, A. W. Yu, Y. Wang, and B. Li · 2018
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Diversity regularized spatiotemporal attention for video-based person re-identification
S. Li, S. Bak, P. Carr, and X. Wang · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Autoregressive quantile networks for generative modeling
G. Ostrovski, W. Dabney, and R. Munos · 2018
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Deep image prior
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2018
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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group
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Implicit generation and generalization in energy-based models
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Simple black-box adversarial attacks
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The singular values of convolutional layers
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