(† Equal contributions)
Preferred Networks Inc.
In this paper, we propose a generative model, Temporal
Generative Adversarial Nets (TGAN), which can learn a semantic
representation of unlabeled videos, and is capable of
generating videos. Unlike existing Generative Adversarial
Nets (GAN)-based methods that generate videos with a single
generator consisting of 3D deconvolutional layers, our
model exploits two different types of generators: a temporal
generator and an image generator. The temporal generator
takes a single latent variable as input and outputs a set of
latent variables, each of which corresponds to an image
frame in a video. The image generator transforms a set of
such latent variables into a video. To deal with instability
in training of GAN with such advanced networks, we adopt
a recently proposed model, Wasserstein GAN, and propose
a novel method to train it stably in an end-to-end manner.
The experimental results demonstrate the effectiveness of our
Results on Video Generation
Following results are random samples generated by TGAN (not cherry picked).
Our model consists of Generator and Discriminator alike usual GANs.
Unlike previous video generating GANs using 3D convolution layers,
we decompose the Generator into 1D convolution + 2D convolution as illustrates below:
The video generator consists of two generators, the temporal generator \(G_0\) and the image generator \(G_1\).
The temporal generator \(G_0\) yields a set of latent variables \(z^t_1(t = 1, . . . , T)\) from \(z_0\).
The image generator \(G_1\) transforms those latent variables \(z^t_1(t = 1, . . . , T)\) and \(z_0\)
into a video data which has \(T\) frames.
The discriminator consists of three-dimensional convolutional layers,
and evaluates whether these frames are from the dataset or the video generator.
Singular Value Clipping
We use Wasserstein GAN objective to train the model.
WGAN requires the discriminator to fulfill the K-Lipschitz constraint, and the authors employed
a parameter clipping method that clamps the weights in the discriminator to [−c, c].
However, we empirically observed that the tuning of hyper parameter c is severe, and it
frequently fails in learning under a different situation like our proposed model.
Instead of the original WGAN, we add a constraint to all linear and convolutional layers in the discriminator that satisfies
the spectral norm of weight parameter W is equal or less than one to satisfy K-Lipschitz constraint.
This means that the singular values of weight matrix are all one or less.
To implement this, we perform singular value decomposition after parameter update,
replace all the singular values larger than one with one,
and reconstruct the parameter with them, which we call Singular Value Clipping (SVC).
- Generative Adversarial Networks, Goodfellow et al., 2014
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al., 2015
- Wasserstein GAN, Arjovsky et al., 2017
- Improved Training of Wasserstein GANs, Gulrajani et al., 2017
- Spectral Normalization for Generative Adversarial Networks, Miyato et al., 2017
- Video Pixel Networks, Kalchbrenner et al., 2016
- Generating Videos with Scene Dynamics, Vondrick et al., 2016
We would like to thank Brian Vogel,
Jethro Tan, Tommi Kerola, and Zornitsa Kostadinova for