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Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images.
Age classification from facial images
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Sex classification is better with three-dimensional head structure than with image intensity information
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Boosting sex identification performance
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
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Clothing cosegmentation for recognizing people
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Understanding images of groups of people
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Face age classification on consumer images with gabor feature and fuzzy lda method
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A study on automatic age estimation using a large database
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How to explain individual classification decisions
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Layer-wise analysis of deep networks with gaussian kernels
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Imagenet classification with deep convolutional neural networks
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Age and gender estimation of unfiltered faces
E. Eidinger, R. Enbar, and T. Hassner · 2014
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Caffe: Convolutional architecture for fast feature embedding
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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Chalearn looking at people 2015: Apparent age and cultural event recognition datasets and results
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Why should i trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Deep expectation of real and apparent age from a single image without facial landmarks
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Mastering the game of go with deep neural networks and tree search
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Interpretable deep neural networks for single-trial eeg classification
I. Sturm, S. Lapuschkin, W. Samek, and K.-R. Müller · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
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Effective face frontalization in unconstrained images
T. Hassner, S. Harel, E. Paz, and R. Enbar · 2015
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Age and gender classification using convolutional neural networks
G. Levi and T. Hassner · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, et al · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Identifying individual facial expressions by deconstructing a neural network
F. Arbabzadeh, G. Montavon, K.-R. Müller, and W. Samek · 2016
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Controlling explanatory heatmap resolution and semantics via decomposition depth
S. Bach, A. Binder, K.-R. Müller, and W. Samek · 2016
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Theano Development Team · 2016
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A new method to visualize deep neural networks
L. M. Zintgraf, T. S. Cohen, and M. Welling · 2016
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Dager: Deep age, gender and emotion recognition using convolutional neural network
A. Dehghan, E. G. Ortiz, G. Shu, and S. Z. Masood · 2017
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Beating the world’s best at super smash bros. with deep reinforcement learning
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Explaining nonlinear classification decisions with deep taylor decomposition
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Methods for interpreting and understanding deep neural networks
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Deepstack: Expert-level artificial intelligence in no-limit poker
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Evaluating the visualization of what a deep neural network has learned
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Quantum-chemical insights from deep tensor neural networks
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