A fundamental, and still largely unanswered, question in the context of
Generative Adversarial Networks (GANs) is whether GANs are actually able to
capture the key characteristics of the datasets they are trained on. The
current approaches to examining this issue require significant human
supervision, such as visual inspection of sampled images, and often offer only
fairly limited scalability. In this paper, we propose new techniques that
employ a classification-based perspective to evaluate synthetic GAN
distributions and their capability to accurately reflect the essential
properties of the training data. These techniques require only minimal human
supervision and can easily be scaled and adapted to evaluate a variety of
state-of-the-art GANs on large, popular datasets. Our analysis indicates that
GANs have significant problems in reproducing the more distributional
properties of the training dataset. In particular, when seen through the lens
of classification, the diversity of GAN data is orders of magnitude less than
that of the original data.
Description
A Classification-Based Perspective on GAN Distributions
%0 Generic
%1 santurkar2017classificationbased
%A Santurkar, Shibani
%A Schmidt, Ludwig
%A Mądry, Aleksander
%D 2017
%K GAN to_read
%T A Classification-Based Perspective on GAN Distributions
%U http://arxiv.org/abs/1711.00970
%X A fundamental, and still largely unanswered, question in the context of
Generative Adversarial Networks (GANs) is whether GANs are actually able to
capture the key characteristics of the datasets they are trained on. The
current approaches to examining this issue require significant human
supervision, such as visual inspection of sampled images, and often offer only
fairly limited scalability. In this paper, we propose new techniques that
employ a classification-based perspective to evaluate synthetic GAN
distributions and their capability to accurately reflect the essential
properties of the training data. These techniques require only minimal human
supervision and can easily be scaled and adapted to evaluate a variety of
state-of-the-art GANs on large, popular datasets. Our analysis indicates that
GANs have significant problems in reproducing the more distributional
properties of the training dataset. In particular, when seen through the lens
of classification, the diversity of GAN data is orders of magnitude less than
that of the original data.
@misc{santurkar2017classificationbased,
abstract = {A fundamental, and still largely unanswered, question in the context of
Generative Adversarial Networks (GANs) is whether GANs are actually able to
capture the key characteristics of the datasets they are trained on. The
current approaches to examining this issue require significant human
supervision, such as visual inspection of sampled images, and often offer only
fairly limited scalability. In this paper, we propose new techniques that
employ a classification-based perspective to evaluate synthetic GAN
distributions and their capability to accurately reflect the essential
properties of the training data. These techniques require only minimal human
supervision and can easily be scaled and adapted to evaluate a variety of
state-of-the-art GANs on large, popular datasets. Our analysis indicates that
GANs have significant problems in reproducing the more distributional
properties of the training dataset. In particular, when seen through the lens
of classification, the diversity of GAN data is orders of magnitude less than
that of the original data.},
added-at = {2018-04-12T14:32:32.000+0200},
author = {Santurkar, Shibani and Schmidt, Ludwig and Mądry, Aleksander},
biburl = {https://www.bibsonomy.org/bibtex/2d9e70003f64d20d76a2dee463b0dda5f/jk_itwm},
description = {A Classification-Based Perspective on GAN Distributions},
interhash = {8b5e8f9308a0bb5902ce1f20a2fad562},
intrahash = {d9e70003f64d20d76a2dee463b0dda5f},
keywords = {GAN to_read},
note = {cite arxiv:1711.00970},
timestamp = {2018-04-12T14:32:32.000+0200},
title = {A Classification-Based Perspective on GAN Distributions},
url = {http://arxiv.org/abs/1711.00970},
year = 2017
}