A Framework for the Quantitative Evaluation of Disentangled Representations
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[1] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] William Whitney. Disentangled Representations in Neural Models , 2016, ArXiv.
[3] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[4] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[5] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[6] Kristen Grauman,et al. Learning Image Representations Tied to Ego-Motion , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[8] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[9] Joshua B. Tenenbaum,et al. Understanding Visual Concepts with Continuation Learning , 2016, ArXiv.
[10] N. L. Johnson,et al. Multivariate Analysis , 1958, Nature.
[11] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[12] Vighnesh Birodkar,et al. Unsupervised Learning of Disentangled Representations from Video , 2017, NIPS.
[13] Serge J. Belongie,et al. Bayesian representation learning with oracle constraints , 2015, ICLR 2016.
[14] Charles Blundell,et al. Early Visual Concept Learning with Unsupervised Deep Learning , 2016, ArXiv.
[15] Shun-ichi Amari,et al. Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.
[16] Yuting Zhang,et al. Learning to Disentangle Factors of Variation with Manifold Interaction , 2014, ICML.
[17] Andrea Vedaldi,et al. Understanding Image Representations by Measuring Their Equivariance and Equivalence , 2014, International Journal of Computer Vision.
[18] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[19] Christopher K. I. Williams,et al. Transformation Equivariant Boltzmann Machines , 2011, ICANN.
[20] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[21] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Quoc V. Le,et al. Measuring Invariances in Deep Networks , 2009, NIPS.
[23] Max Welling,et al. Learning the Irreducible Representations of Commutative Lie Groups , 2014, ICML.
[24] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[25] Xiaogang Wang,et al. Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations , 2014, NIPS.
[26] Bruno A. Olshausen,et al. Discovering Hidden Factors of Variation in Deep Networks , 2014, ICLR.
[27] Pushmeet Kohli,et al. Overcoming Occlusion with Inverse Graphics , 2016, ECCV Workshops.
[28] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[29] Yoshua Bengio,et al. Disentangling Factors of Variation via Generative Entangling , 2012, ArXiv.
[30] Scott E. Reed,et al. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis , 2015, NIPS.
[31] Max Welling,et al. Transformation Properties of Learned Visual Representations , 2014, ICLR.
[32] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[33] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[34] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.