user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN

Paper content:
We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively.
This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization.
The inherent connection does not only provide a theoretical convergence proof for training GANs in the function space, but also inspires a novel objective function for training.
The modified objective function forces the distribution of generator outputs to be updated along the direction according to the primal-dual subgradient methods.
A toy example shows that the proposed method is able to resolve mode collapse, which in this case cannot be avoided by the standard GAN or Wasserstein GAN.
Experiments on both Gaussian mixture synthetic data and real-world image datasets demonstrate the performance of the proposed method on generating diverse samples.
Generative adversarial networks (GANs) are a class of game theoretical methods for learning data distributions.
It trains the generative model by maintaining two deep neural networks, namely the discriminator network D and the generator network G. The generator aims to produce samples resembling real data samples, while the discriminator aims to distinguish the generated samples and real data samples.The standard GAN training procedure is formulated as the following minimax game: DISPLAYFORM0 where p d (x) is the data distribution and p z (z) is the noise distribution.
The generated samples G(z) induces a generated distribution p g (x).
Theoretically, the optimal solution to (1) is p * g = p d and D * (x) = 1/2 for all x in the support of data distribution.In practice, the discriminator network and the generator network are parameterized by θ θ θ d and θ θ θ g , respectively.
The neural network parameters are updated iteratively according to gradient descent.
In particular, the discriminator is first updated either with multiple gradient descent steps until convergence or with a single gradient descent step, then the generator is updated with a single descent step.
However, the analysis of the convergence properties on the training approaches is challenging, as noted by Ian Goodfellow in BID10 , "For GANs, there is no theoretical prediction as to whether simultaneous gradient descent should converge or not. Settling this theoretical question, and developing algorithms guaranteed to converge, remain important open research problems.".
There have been some recent studies on the convergence behaviours of GAN training (Nowozin et al., 2016; BID18 BID14 BID24 BID22 .The
simultaneous gradient descent method is proved to converge assuming the objective function is convex-concave in the network parameters (Nowozin et al., 2016) . The
local stability property is established in BID14 BID24 .One
notable inconvergence issue with GAN training is referred to as mode collapse, where the generator characterizes only a few modes of the true data distribution BID11 BID18 . Various
methods have been proposed to alleviate the mode collapse problem. Feature
matching for intermediate layers of the discriminator has been proposed in (Salimans et al., 2016) . In BID23
, the generator is updated based on a sequence of previous unrolled discriminators. A mixture
of neural networks are used to generate diverse samples (Tolstikhin et al., 2017; BID15 BID2 . In , it was
proposed that adding noise perturbation on the inputs to the discriminator can alleviate the mode collapse problem. It is shown
that this training-with-noise technique is equivalent to adding a regularizer on the gradient norm of the discriminator (Roth et al., 2017) . The Wasserstein
divergence is proposed to resolve the problem of incontinuous divergence when the generated distribution and the data distribution have disjoint supports BID12 . Mode regularization
is used in the loss function to penalize the missing modes BID6 Srivastava et al., 2017) . The regularization
is usually based on heuristics, which tries to minimize the distance between the data samples and the generated samples, but lacks theoretical convergence guarantee.In this paper, we formulate the minimax optimization for GAN training (1) as finding the saddle points of the Lagrangian function for a convex optimization problem. In the convex optimization
problem, the discriminator function D(·) and the probabilities of generator outputs p g (·) play the roles of the primal variables and dual variables, respectively. This connection not only provides
important insights in understanding the convergence of GAN training, but also enables us to leverage the primal-dual subgradient methods to design a novel objective function that helps to alleviate mode collapse. A toy example reveals that for some
cases when standard GAN or WGAN inevitably leads to mode collapse, our proposed method can effectively avoid mode collapse and converge to the optimal point.In this paper, we do not aim at achieving superior performance over other GANs, but rather provide a new perspective of understanding GANs, and propose an improved training technique that can be applied on top of existing GANs. The contributions of the paper are
as follows:• The standard training of GANs in the function space is formulated as primal-dual subgradient methods for solving convex optimizations.• This formulation enables us to show
that with a proper gradient descent step size, updating the discriminator and generator probabilities according to the primal-dual algorithms will provably converge to the optimal point.• This formulation results in a novel
training objective for the generator. With the proposed objective function,
the generator is updated such that the probabilities of generator outputs are pushed to the optimal update direction derived by the primal-dual algorithms. Experiments have shown that this simple
objective function can effectively alleviate mode collapse in GAN training.• The convex optimization framework incorporates
different variants of GANs including the family of f -GAN (Nowozin et al., 2016) and an approximate variant of WGAN. For all these variants, the training objective can
be improved by including the optimal update direction of the generated probabilities.
In this paper, we propose a primal-dual formulation for generative adversarial learning.
This formulation interprets GANs from the perspective of convex optimization, and gives the optimal update of the discriminator and the generated distribution with convergence guarantee.
By framing different variants of GANs under the convex optimization framework, the corresponding training algorithms can all be improved by pushing the generated distribution along the optimal direction.
Experiments on two synthetic datasets demonstrate that the proposed formulation can effectively avoid mode collapse.
It also achieves competitive quantitative evaluation scores on two benchmark real-world image datasets.
The proof of convergence for dual-driven algorithms can be found in BID4 , Chapter 3).The
primal-dual-driven algorithm for continuous time update has been studied in BID8 . Here
, we show the convergence for the discrete-time case.We choose a step size α(t) that satisfies DISPLAYFORM0 Let z(t) = [x(t), λ λ λ(t)] T be a vector consisting of the primal and dual variables at the t-th iteration. The
primal-dual-driven update can be expressed as: DISPLAYFORM1 where DISPLAYFORM2 and DISPLAYFORM3 Since the subgradient is bounded by assumption, there exists M > 0 such that ||T (·)|| 2 2 < M , where ||.|| 2
stands for the L 2 norm.

assistant: text: We propose a primal-dual subgradient method for training GANs and this method effectively alleviates mode collapse.