**TGAN** is a type of generative adversarial network that is capable of learning representation from an unlabeled video dataset and producing a new video. The generator consists of two sub networks
called a temporal generator and an image generator. Specifically, the temporal generator first yields a set of latent variables, each of which corresponds to a latent variable for the image generator. Then, the image generator transforms these latent variables into a video which has the same number of frames as the variables. The model comprised of the temporal and image generators can not only enable to efficiently capture the time series, but also be easily extended to frame interpolation. The authors opt for a [WGAN](https://paperswithcode.com/method/wgan) as the basic [GAN](https://paperswithcode.com/method/gan) structure and objective, but use [singular value clipping](https://paperswithcode.com/method/singular-value-clipping) to enforce the Lipschitz constraint.

A **Dual Path Network** block is an image model block used in convolutional neural network. The idea of this module is to enable sharing of common features while maintaining the flexibility to explore new features through dual path architectures. In this sense it combines the benefits of [ResNets](https://paperswithcode.com/method/resnet) and [DenseNets](https://paperswithcode.com/method/densenet). It was proposed as part of the [DPN](https://paperswithcode.com/method/dpn) CNN architecture.

We formulate such a dual path architecture as follows:

$$x^{k} = \sum\limits\_{t=1}^{k-1} f\_t^{k}(h^t) \text{,}  $$

$$
y^{k} = \sum\limits\_{t=1}^{k-1} v\_t(h^t) = y^{k-1} + \phi^{k-1}(y^{k-1}) \text{,} \\\\
$$

$$
r^{k} = x^{k} + y^{k} \text{,} \\\\
$$

$$
h^k = g^k \left( r^{k} \right) \text{,}
$$

where $x^{k}$ and $y^{k}$ denote the extracted information at $k$-th step from individual path, $v_t(\cdot)$ is a feature learning function as $f_t^k(\cdot)$. The first equation refers to the densely connected path that enables exploring new features. The second equation refers to the residual path that enables common features re-usage. The third equation defines the dual path that integrates them and feeds them to the last transformation function in the last equation.

DPN Block

Dual Path Networks

TGAN

Temporal Generative Adversarial Nets with Singular Value Clipping

The training contains two steps. The first step is to train data for creating the T2D and D2T models. Then, in the second step, use these
models to infer self-memory for self-training the D2T model.  Then, the training takes self-memory and new data for self-training the D2T model but not the T2D model.

Source	Temporal Generative Adversarial Nets with Singular Value Clipping
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com