Geometry of Deep Generative Models for Disentangled Representations-Reference-Cited by-同舟云学术

Geometry of Deep Generative Models for Disentangled Representations

Published:2018-12-18 Issue: Volume: Page:
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Container-title:Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing
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Author:

Shukla Ankita¹,Uppal Shagun¹,Bhagat Sarthak¹,Anand Saket¹,Turaga Pavan²

Affiliation:

1. IIIT-Delhi, Delhi, India

2. Arizona State University, Tempe, Arizona, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3293353.3293422

Reference22 articles.

1. Georgios Arvanitidis Lars Kai Hansen and Søren Hauberg. 2017. Latent space oddity: on the curvature of deep generative models. arXiv preprint arXiv:1710.11379 (2017). Georgios Arvanitidis Lars Kai Hansen and Søren Hauberg. 2017. Latent space oddity: on the curvature of deep generative models. arXiv preprint arXiv:1710.11379 (2017).

2. Yoshua Bengio Aaron Courville and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35 8 (2013) 1798--1828. Yoshua Bengio Aaron Courville and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35 8 (2013) 1798--1828.

3. Why Deep Learning Works: A Manifold Disentanglement Perspective

4. Brian Cheung Jesse A Livezey Arjun K Bansal and Bruno A Olshausen. 2014. Discovering hidden factors of variation in deep networks. arXiv preprint arXiv:1412.6583 (2014). Brian Cheung Jesse A Livezey Arjun K Bansal and Bruno A Olshausen. 2014. Discovering hidden factors of variation in deep networks. arXiv preprint arXiv:1412.6583 (2014).

5. From few to many: illumination cone models for face recognition under variable lighting and pose

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