Title: A VARIATIONAL INEQUALITY PERSPECTIVE ON GENERATIVE ADVERSARIAL NETWORKS

Abstract: Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend techniques designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.

1. INTRODUCTION:

Generative adversarial networks (GANs) (Goodfellow et al., 2014) form a generative modeling approach known for producing realistic natural images (Karras et al., 2018) as well as high quality super-resolution (Ledig et al., 2017) and style transfer (Zhu et al., 2017) . Nevertheless, GANs are also known to be difficult to train, often displaying an unstable behavior (Goodfellow, 2016) . Much recent work has tried to tackle these training difficulties, usually by proposing new formulations of the GAN objective (Nowozin et al., 2016; Arjovsky et al., 2017) . Each of these formulations can be understood as a two-player game, in the sense of game theory (Von Neumann and Morgenstern, 1944) , and can be addressed as a variational inequality problem (VIP) (Harker and Pang, 1990) , a framework that encompasses traditional saddle point optimization algorithms (Korpelevich, 1976) .

Solving such GAN games is traditionally approached by running variants of stochastic gradient descent (SGD) initially developed for optimizing supervised neural network objectives. Yet it is known that for some games (Goodfellow, 2016, §8. 2) SGD exhibits oscillatory behavior and fails to converge. This oscillatory behavior, which does not arise from stochasticity, highlights a fundamental problem: while a direct application of basic gradient descent is an appropriate method for regular minimization problems, it is not a sound optimization algorithm for the kind of two-player games of GANs. This constitutes a fundamental issue for GAN training, and calls for the use of more principled methods with more reassuring convergence guarantees.

Contributions. We point out that multi-player games can be cast as variational inequality problems (VIPs) and consequently the same applies to any GAN formulation posed as a minimax or non-zerosum game. We present two techniques from this literature, namely averaging and extrapolation, widely used to solve VIPs but which have not been explored in the context of GANs before. 1We extend standard GAN training methods such as SGD or Adam into variants that incorporate these techniques (Alg. 4 is new). We also explain that the oscillations of basic SGD for GAN training previously noticed (Goodfellow, 2016) can be explained by standard variational inequality optimization results and we illustrate how averaging and extrapolation can fix this issue.

We introduce a technique, called extrapolation from the past, that only requires one gradient computation per update compared to extrapolation which requires to compute the gradient twice, rediscovering, with a VIP perspective, a particular case of optimistic mirror descent (Rakhlin and Sridharan, 2013) . We prove its convergence for strongly monotone operators and in the stochastic VIP setting.

Finally, we test these techniques in the context of GAN training. We observe a 4-6% improvement over Miyato et al. (2018) on the inception score and the Fréchet inception distance on the CIFAR-10 dataset using a WGAN-GP (Gulrajani et al., 2017) and a ResNet generator. 2Outline. §2 presents the background on GAN and optimization, and shows how to cast this optimization as a VIP. §3 presents standard techniques and extrapolation from the past to optimize variational inequalities in a batch setting. §4 considers these methods in the stochastic setting, yielding three corresponding variants of SGD, and provides their respective convergence rates. §5 develops how to combine these techniques with already existing algorithms. §6 discusses the related work and §7 presents experimental results.

2. GAN OPTIMIZATION AS A VARIATIONAL INEQUALITY PROBLEM:



2.1. GAN FORMULATIONS:

The purpose of generative modeling is to generate samples from a distribution q θ that matches best the true distribution p of the data. The generative adversarial network training strategy can be understood as a game between two players called generator and discriminator. The former produces a sample that the latter has to classify between real or fake data. The final goal is to build a generator able to produce sufficiently realistic samples to fool the discriminator.

In the original GAN paper (Goodfellow et al., 2014) , the GAN objective is formulated as a zero-sum game where the cost function of the discriminator D ϕ is given by the negative log-likelihood of the binary classification task between real or fake data generated from q θ by the generator, (1)

However Goodfellow et al. (2014) recommends to use in practice a second formulation, called non-saturating GAN. This formulation is a non-zero-sum game where the aim is to jointly minimize:

L G (θ, ϕ) def = -E x ∼q θ log D ϕ (x ) and L D (θ, ϕ) def = -E x∼p log D ϕ (x) -E x ∼q θ log(1 -D ϕ (x )) . (2)

The dynamics of this formulation has the same stationary points as the zero-sum one (1) but is claimed to provide "much stronger gradients early in learning" (Goodfellow et al., 2014) .

2.2. EQUILIBRIUM:

The minimax formulation (1) is theoretically convenient because a large literature on games studies this problem and provides guarantees on the existence of equilibria. Nevertheless, practical considerations lead the GAN literature to consider a different objective for each player as formulated in (2).

In that case, the two-player game problem (Von Neumann and Morgenstern, 1944) consists in finding the following Nash equilibrium:

EQUATION

Only when L G = -L D is the game called a zero-sum game and (3) can be formulated as a minimax problem. One important point to notice is that the two optimization problems in (3) are coupled and have to be considered jointly from an optimization point of view.

Standard GAN objectives are non-convex (i.e. each cost function is non-convex), and thus such (pure) equilibria may not exist. As far as we know, not much is known about the existence of these equilibria for non-convex losses (see Heusel et al. (2017) and references therein for some results). In our theoretical analysis in §4, our assumptions (monotonicity (24) of the operator and convexity of the constraint set) imply the existence of an equilibrium.

In this paper, we focus on ways to optimize these games, assuming that an equilibrium exists. As is often standard in non-convex optimization, we also focus on finding points satisfying the necessary stationary conditions. As we mentioned previously, one difficulty that emerges in the optimization of such games is that the two different cost functions of (3) have to be minimized jointly in θ and ϕ.

Fortunately, the optimization literature has for a long time studied so-called variational inequality problems, which generalize the stationary conditions for two-player game problems.

2.3. VARIATIONAL INEQUALITY PROBLEM FORMULATION:

We first consider the local necessary conditions that characterize the solution of the smooth two-player game (3), defining stationary points, which will motivate the definition of a variational inequality. In the unconstrained setting, a stationary point is a couple (θ * , ϕ * ) with zero gradient:

EQUATION

When constraints are present,3 a stationary point (θ * , ϕ * ) is such that the directional derivative of each cost function is non-negative in any feasible direction (i.e. there is no feasible descent direction):

EQUATION

)

Defining 5) can be compactly formulated as:

ω def = (θ, ϕ), ω * def = (θ * , ϕ * ), Ω def = Θ × Φ, Eq. (

F (ω * ) (ω -ω * ) ≥ 0 , ∀ω ∈ Ω where F (ω) def = ∇ θ L G (θ, ϕ) ∇ ϕ L D (θ, ϕ) . (6)

These stationary conditions can be generalized to any continuous vector field: let Ω ⊂ R d and F : Ω → R d be a continuous mapping. The variational inequality problem (Harker and Pang, 1990 ) (depending on F and Ω) is:

find ω * ∈ Ω such that F (ω * ) (ω -ω * ) ≥ 0 , ∀ω ∈ Ω . (VIP)

We call optimal set the set Ω * of ω ∈ Ω verifying (VIP). The intuition behind it is that any ω * ∈ Ω * is a fixed point of the constrained dynamic of F (constrained to Ω).

We have thus showed that both saddle point optimization and non-zero sum game optimization, which encompass the large majority of GAN variants proposed in the literature, can be cast as VIPs. In the next section, we turn to suitable optimization techniques for such problems.

3. OPTIMIZATION OF VARIATIONAL INEQUALITIES (BATCH SETTING):

Let us begin by looking at techniques that were developed in the optimization literature to solve VIPs. We present the intuitions behind them as well as their performance on a simple bilinear problem (see Fig. 1 ). Our goal is to provide mathematical insights on averaging ( §3.1) and extrapolation ( §3.2) and propose a novel variant of the extrapolation technique that we called extrapolation from the past ( §3.3). We consider the batch setting, i.e., the operator F (ω) defined in Eq. 6 yields an exact full gradient. We present extensions of these techniques to the stochastic setting later in §4.

The two standard methods studied in the VIP literature are the gradient method (Bruck, 1977) and the extragradient method (Korpelevich, 1976) . The iterates of the basic gradient method are given by ω t+1 = P Ω [ω t -ηF (ω t )] where P Ω [•] is the projection onto the constraint set (if constraints are present) associated to (VIP). These iterates are known to converge linearly under an additional assumption on the operator4 (Chen and Rockafellar, 1997) , but oscillate for a bilinear operator as shown in Fig. 1 . On the other hand, the uniform average of these iterates converge for any bounded monotone operator with a O(1/ √ t) rate (Nedić and Ozdaglar, 2009) , motivating the presentation of averaging in §3.1. By contrast, the extragradient method (extrapolated gradient) does not require any averaging to converge for monotone operators (in the batch setting), and can even converge at the faster O(1/t) rate (Nesterov, 2007) . The idea of this method is to compute a lookahead step (see intuition on extrapolation in §3.2) in order to compute a more stable direction to follow.

3.1. AVERAGING:

More generally, we consider a weighted averaging scheme with weights ρ t ≥ 0. This weighted averaging scheme have been proposed for the first time for (batch) VIP by Bruck (1977) ,

EQUATION

Averaging schemes can be efficiently implemented in an online fashion noticing that, ωT = (1 -ρT ) ωT -1 + ρT ω T where 0 ≤ ρT ≤ 1 .

For instance, setting ρT = 1 T yields uniform averaging (ρ t = 1) and ρt = 1 -β < 1 yields geometric averaging, also known as exponential moving averaging

(ρ t = β T -t , 1 ≤ t ≤ T ).

Averaging is experimentally compared with the other techniques presented in this section in Fig. 1 .

In order to illustrate how averaging tackles the oscillatory behavior in game optimization, we consider a toy example where the discriminator and the generator are linear: D ϕ (x) = ϕ T x and G θ (z) = θz (implicitly defining q θ ). By substituting these expressions in the WGAN objective,5 we get the following bilinear objective:

EQUATION

A similar task was presented by Nagarajan and Kolter (2017) where they consider a quadratic discriminator instead of a linear one, and show that gradient descent is not necessarily asymptotically stable. The bilinear objective has been extensively used (Goodfellow, 2016; Mescheder et al., 2018; Yadav et al., 2018) to highlight the difficulties of gradient descent for saddle point optimization.

Yet, ways to cope with this issue have been proposed decades ago in the context of mathematical programming. For illustrating the properties of the methods of interest, we will study their behavior in the rest of §3 on a simple unconstrained unidimensional version of Eq. 9 (this behavior can be generalized to general multidimensional bilinear examples, see §B.3):

EQUATION

The operator associated with this minimax game is F (θ, φ) = (φ, -θ). There are several ways to compute the discrete updates of this dynamics. The two most common ones are the simultaneous and the alternating gradient update rules, Simultaneous update:

EQUATION

Interestingly, these two choices give rise to completely different behaviors. The norm of the simultaneous updates diverges geometrically, whereas the alternating iterates are bounded but do not converge to the equilibrium. As a consequence, their respective uniform average have a different behavior, as highlighted in the following proposition (proof in §B.1 and generalization in §B.3): Proposition 1. The simultaneous iterates diverge geometrically and the alternating iterates defined in (11) are bounded but do not converge to 0 as

Simultaneous: θ 2 t+1 + φ 2 t+1 = (1 + η 2 )(θ 2 t + φ 2 t ) , Alternating: θ 2 t + φ 2 t = Θ(θ 2 0 + φ 2 0 ) (12) where u t = Θ(v t ) ⇔ ∃α, β, t 0 > 0 such that ∀t ≥ t 0 , αv t ≤ u t ≤ βv t .

The uniform average ( θt , φt ) def = 1 t t-1 s=0 (θ s , φ s ) of the simultaneous updates (resp. the alternating updates) diverges (resp. converges to 0) as,

EQUATION

)

This sublinear convergence result, proved in §B, underlines the benefits of averaging when the sequence of iterates is bounded (i.e. for alternating update rule). When the sequence of iterates is not bounded (i.e. for simultaneous updates) averaging fails to ensure convergence. This theorem also shows how alternating updates may have better convergence properties than simultaneous updates.

3.2. EXTRAPOLATION:

Another technique used in the variational inequality literature to prevent oscillations is extrapolation. This concept is anterior to the extragradient method since Korpelevich (1976) mentions that the idea of extrapolated "prices" to give "stability" had been already formulated by Polyak (1963, Chap. II) . The idea behind this technique is to compute the gradient at an (extrapolated) point different from the current point from which the update is performed, stabilizing the dynamics:

Compute extrapolated point:

EQUATION

Perform update step:

EQUATION

Note that, even in the unconstrained case, this method is intrinsically different from Nesterov's momentum6 (Nesterov, 1983 , Eq. 2.2.9) because of this lookahead step for the gradient computation:

Nesterov's method:

EQUATION

)

Nesterov's method does not converge when trying to optimize (10). One intuition of why extrapolation has better convergence properties than the standard gradient method comes from Euler's integration framework. Indeed, to first order, we have ω t+1/2 ≈ ω t+1 + o(η) and consequently, the update step (15) can be interpreted as a first order approximation to an implicit method step:

EQUATION

)

Implicit methods are known to be more stable and to benefit from better convergence properties (Atkinson, 2003) than explicit methods, e.g., in §B.2 we show that ( 17) on (10) converges for any η. Though, they are usually not practical since they require to solve a potentially non-linear system at each step.

Going back to the simplified WGAN toy example (10) from §3.1, we get the following update rules:

EQUATION

In the following proposition, we see that for η < 1, the respective convergence rates of the implicit method and extrapolation are highly similar. Keeping in mind that the latter has the major advantage of being more practical, this proposition clearly underlines the benefits of extrapolation. Note that Prop. 1 and 2 generalize to general unconstrained bilinear game (more details and proof in §B.3), Proposition 2. The squared norm of the iterates N 2 t def = θ 2 t + φ 2 t , where the update rule of θ t and φ t are defined in (18), decreases geometrically for any η < 1 as,

Implicit: N 2 t+1 = 1 -η 2 + η 4 + O(η 6 ) N 2 t , Extrapolation: N 2 t+1 = (1 -η 2 + η 4 )N 2 t . (19)

3.3. EXTRAPOLATION FROM THE PAST:

One issue with extrapolation is that the algorithm "wastes" a gradient ( 14). Indeed we need to compute the gradient at two different positions for every single update of the parameters. We thus propose a technique that we call extrapolation from the past that only requires a single gradient computation per update. The idea is to store and re-use the extrapolated gradient for the extrapolation:

Extrapolation from the past:

EQUATION

Perform update step: ω t+1 = P Ω [ω t -ηF (ω t+1/2 )] and store:

EQUATION

The same update scheme was proposed by Chiang et al. (2012, Alg. 1) in the context of online convex optimization and generalized by Rakhlin and Sridharan (2013) for general online learning. Without projection, (20) and ( 21) reduce to the optimistic mirror descent described by Daskalakis et al. (2018) :

Optimistic mirror descent (OMD):

EQUATION

We rediscovered this technique from a different perspective: it was motivated by VIP and inspired from the extragradient method. Using the VIP point of view, we are able to prove a linear convergence rate for extrapolation from the past (see details and proof of Theorem 1 in §B.4). We also provide results for a stochastic version in §4. In comparison to the results from Daskalakis et al. (2018) that Adam) with the techniques presented in §3 on the optimization of (9). Only the algorithms advocated in this paper (Averaging, Extrapolation and Extrapolation from the past) converge quickly to the solution. Each marker represents 20 iterations.

We compare these algorithms on a non-convex objective in §G.1.

Algorithm 1 AvgSGD:

Let

ω 0 ∈ Ω for t = 0 . . . T -1 do ξ t ∼ P (mini-batch) d t ← F (ω t , ξ t ) ω t+1 ← P Ω [ω t -η t d t ] end for Return ωT ← T -1 t=0 ηtωt T -1 t=0 ηt Algorithm 2 AvgExtraSGD for t = 0 . . . T -1 do ξ t , ξ t ∼ P (mini-batches) d t ← F (ω t , ξ t ) ω t ← P Ω [ω t -η t d t ] d t ← F (ω t , ξ t ) ω t+1 ← P Ω [ω t -η t d t ] end for Return ωT ← T -1 t=0 ηtω t T -1 t=0 ηt Algorithm 3 AvgPastExtraSGD Let ω 0 ∈ Ω for t = 0 . . . T -1 do ξ t ∼ P (mini-batch) ω t ← P Ω [ω t -η t d t-1 ] d t ← F (ω t , ξ t ) ω t+1 ← P Ω [ω t -η t d t ] end for Return ωT ← T -1 t=0 ηtω t T -1 t=0 ηt

Figure 2: Three variants of SGD computing T updates, using the techniques introduced in §3.

hold only for a bilinear objective, we provide a faster convergence rate (linear vs sublinear) on the last iterate for a general (strongly monotone) operator F and any projection on a convex Ω. One thing to notice is that the operator of a bilinear objective is not strongly monotone, but in that case one can use the standard extrapolation method (14) which converges linearly for a (constrained or not) bilinear game (Tseng, 1995, Cor. 3.3) . Theorem 1 (Linear convergence of extrapolation from the past). If F is µ-strongly monotone (see §A for the definition of strong monotonicity) and L-Lipschitz, then the updates (20) and (21) with η = 1 4L provide linearly converging iterates,

EQUATION

)

4. OPTIMIZATION OF VIP WITH STOCHASTIC GRADIENTS:

In this section, we consider extensions of the techniques presented in §3 to the context of a stochastic operator, i.e., we no longer have access to the exact gradient F (ω) but to an unbiased stochastic estimate of it, F (ω, ξ), where ξ ∼ P and F (ω

) := E ξ∼P [F (ω, ξ)].

It is motivated by GAN training where we only have access to a finite sample estimate of the expected gradient, computed on a mini-batch. For GANs, ξ is a mini-batch of points coming from the true data distribution p and the generator distribution q θ .

For our analysis, we require at least one of the two following assumptions on the stochastic operator:

Assumption 1. Bounded variance by σ 2 : E ξ [ F (ω) -F (ω, ξ) 2 ] ≤ σ 2 , ∀ω ∈ Ω . Assumption 2. Bounded expected squared norm by M 2 : E ξ [ F (ω, ξ) 2 ] ≤ M 2 , ∀ω ∈ Ω.

Assump. 1 is standard in stochastic variational analysis, while Assump. 2 is a stronger assumption sometimes made in stochastic convex optimization. To illustrate how strong Assump. 2 is, note that it does not hold for an unconstrained bilinear objective like in our example (10) in §3. It is thus mainly reasonable for bounded constraint sets. Note that in practice we have σ M .

We now present and analyze three algorithms that are variants of SGD that are appropriate to solve (VIP). The first one Alg. 1 (AvgSGD) is the stochastic extension of the gradient method for solving (VIP); Alg. 2 (AvgExtraSGD) uses extrapolation and Alg. 3 (AvgPastExtraSGD) uses extrapolation from the past. A fourth variant that re-use the mini-batch for the extrapolation step (ReExtraSGD, Alg. 5) is described in §D. These four algorithms return an average of the iterates (typical in stochastic setting). The proofs of the theorems presented in this section are in §F.

To handle constraints such as parameter clipping (Arjovsky et al., 2017) , we gave a projected version of these algorithms, where P Ω [ω ] denotes the projection of ω onto Ω (see §A). Note that when Ω = R d , the projection is the identity mapping (unconstrained setting). In order to prove the convergence of these four algorithms, we will assume that F is monotone:

(F (ω) -F (ω )) (ω -ω ) ≥ 0 ∀ ω, ω ∈ Ω .

(24) If F can be written as ( 6), it implies that the cost functions are convex.7 Note however that general GANs parametrized with neural networks lead to non-monotone VIPs. Assumption 3. F is monotone and Ω is a compact convex set, such that max ω,ω ∈Ω ω-ω 2 ≤ R 2 . In that setting the quantity g(ω * ) := max ω∈Ω F (ω) (ω * -ω) is well defined and is equal to 0 if and only if ω * is a solution of (VIP). Moreover, if we are optimizing a zero-sum game, we have

ω = (θ, ϕ), Ω = Θ × Φ and F (θ, ϕ) = [∇ θ L(θ, ϕ) -∇ ϕ L(θ, ϕ)] . Hence, the quantity h(θ * , ϕ * ) := max ϕ∈Φ L(θ * , ϕ) -min θ∈Θ L(θ, ϕ *

) is well defined and equal to 0 if and only if (θ * , ϕ * ) is a Nash equilibrium of the game. The two functions g and h are called merit functions (more details on the concept of merit functions in §C). In the following, we call,

EQUATION

)

Averaging. Alg. 1 (AvgSGD) presents the stochastic gradient method with averaging, which reduces to the standard (simultaneous) SGD updates for the two-player games used in the GAN literature, but returning an average of the iterates. Theorem 2. Under Assump. 1, 2 and 3, SGD with averaging (Alg. 1) with a constant step-size gives,

EQUATION

Thm. 2 uses a similar proof as (Nemirovski et al., 2009) . The constant term η(M 2 + σ 2 )/2 in ( 26) is called the variance term. This type of bound is standard in stochastic optimization. We also provide in §F a similar Õ(1/ √ t) rate with an extra log factor when η t = η √ t . We show that this variance term is smaller than the one of SGD with prediction method (Yadav et al., 2018) in §E.

Extrapolations. Alg. 2 (AvgExtraSGD) adds an extrapolation step compared to Alg. 1 in order to reduce the oscillations due to the game between the two players. A theoretical consequence is that it has a smaller variance term than (26). As discussed previously, Assump. 2 made in Thm. 2 for the convergence of Alg. 1 is very strong in the unbounded setting. One advantage of SGD with extrapolation is that Thm. 3 does not require this assumption. Theorem 3. (Juditsky et al., 2011, Thm. 1) Under Assump. 1 and 3, if E ξ [F ] is L-Lipschitz, then SGD with extrapolation and averaging (Alg. 2) using a constant step-size η ≤ 1

EQUATION

)

Since in practice σ M , the variance term in ( 27) is significantly smaller than the one in (26). To summarize, SGD with extrapolation provides better convergence guarantees but requires two gradient computations and samples per iteration. This motivates our new method, Alg. 3 (AvgPastExtraSGD) which uses extrapolation from the past and achieves the best of both worlds (in theory). Theorem 4. Under Assump. 1 and 3, if E ξ [F ] is L-Lipschitz then SGD with extrapolation from the past using a constant step-size η ≤ 1 2 √ 3L , gives that the averaged iterates converge as,

EQUATION

)

The bound is similar to the one provided in Thm. 3 but each iteration of Alg. 3 is computationally half the cost of an iteration of Alg. 2.

5. COMBINING THE TECHNIQUES WITH ESTABLISHED ALGORITHMS:

In the previous sections, we presented several techniques that converge for stochastic monotone operators. These techniques can be combined in practice with existing algorithms. We propose to combine them to two standard algorithms used for training deep neural networks: the Adam optimizer (Kingma and Ba, 2015) and the SGD optimizer (Robbins and Monro, 1951) . For the Adam optimizer, there are several possible choices on how to update the moments. This choice can lead to different algorithms in practice: for example, even in the unconstrained case, our proposed Adam with extrapolation from the past (Alg. 4) is different from Optimistic Adam (Daskalakis et al., 2018) (the moments are updated differently). Note that in the case of a two-player game (3), the previous convergence results can be generalized to gradient updates with a different step-size for each player by simply rescaling the objectives L G and L D by a different scaling factor. A detailed pseudo-code for Adam with extrapolation step (Extra-Adam) is given in Algorithm 4. Note that our interest regarding this algorithm is practical and that we do not provide any convergence proof.

Algorithm 4 Extra-Adam: proposed Adam with extrapolation step.

input: step-size η, decay rates for moment estimates β 1 , β 2 , access to the stochastic gradients ∇ t (•) and to the projection P Ω [•] onto the constraint set Ω, initial parameter ω 0 , averaging scheme (ρ t ) t≥1 for t = 0 . . . T -1 do Option 1: Standard extrapolation.

Sample new mini-batch and compute stochastic gradient: g t ← ∇ t (ω t ) Option 2: Extrapolation from the past Load previously saved stochastic gradient:

g t = ∇ t-1/2 (ω t-1/2 ) Update estimate of first moment for extrapolation: m t-1/2 ← β 1 m t-1 + (1 -β 1 )g t Update estimate of second moment for extrapolation: v t-1/2 ← β 2 v t-1 + (1 -β 2 )g 2 t Correct the bias for the moments: mt-1/2 ← m t-1/2 /(1 -β 2t-1 1 ), vt-1/2 ← v t-1/2 /(1 -β 2t-1 2 ) Perform extrapolation step from iterate at time t: ω t-1/2 ← P Ω [ω t -η mt-1/2 √ vt-1/2 + ]

Sample new mini-batch and compute stochastic gradient:

g t+1/2 ← ∇ t+1/2 (ω t+1/2 ) Update estimate of first moment: m t ← β 1 m t-1/2 + (1 -β 1 )g t+1/2

Update estimate of second moment:

v t ← β 2 v t-1/2 + (1 -β 2 )g 2 t+1/2

Compute bias corrected for first and second moment:

mt ← m t /(1 -β 2t 1 ), vt ← v t /(1 -β 2t

2 ) Perform update step from the iterate at time t:

ω t+1 ← P Ω [ω t -η mt √ vt+ ] end for Output: ω T -1/2 , ω T or ωT = T -1 t=0 ρ t+1 ω t+1/2 / T -1 t=0 ρ t+1 (see (8) for online averaging)

6. RELATED WORK:

The extragradient method is a standard algorithm to optimize variational inequalities. This algorithm has been originally introduced by Korpelevich (1976) and extended by Nesterov (2007) and Nemirovski (2004) . Stochastic versions of the extragradient have been recently analyzed (Juditsky et al., 2011; Yousefian et al., 2014; Iusem et al., 2017) for stochastic variational inequalities with bounded constraints. A linearly convergent variance reduced version of the stochastic gradient method has been proposed by Palaniappan and Bach (2016) for strongly monotone variational inequalities. Extrapolation can also be related to optimistic methods (Chiang et al., 2012; Rakhlin and Sridharan, 2013) proposed in the online learning literature (see more details in §3.3). Interesting non-convex results were proved, for a new notion of regret minimization, by Hazan et al. (2017) and in the context of online learning for GANs by Grnarova et al. (2018) .

Several methods to stabilize GANs consist in transforming a zero-sum formulation into a more general game that can no longer be cast as a saddle point problem. This is the case of the non-saturating formulation of GANs (Goodfellow et al., 2014; Fedus et al., 2018) , the DCGANs (Radford et al., 2016) , the gradient penalty8 for WGANs (Gulrajani et al., 2017) . Yadav et al. (2018) propose an optimization method for GANs based on AltSGD using an additional momentum-based step on the generator. Daskalakis et al. (2018) proposed a method inspired from game theory. Li et al. (2017) suggest to dualize the GAN objective to reformulate it as a maximization problem and Mescheder et al. (2017) propose to add the norm of the gradient in the objective to get a better signal. Gidel et al. (2019) analyzed a generalization of the bilinear example ( 9) with a focus put on the effect of momentum on this problem. They do not consider extrapolation (see §B.3 for more details). Unrolling steps (Metz et al., 2017 ) can be confused with extrapolation but is fundamentally different: the perspective is to try to approximate the "true generator objective function" unrolling for K steps the updates of the discriminator and then updating the generator.

Regarding the averaging technique, some recent work appear to have already successfully used geometric averaging (7) for GANs in practice, but only briefly mention it (Karras et al., 2018; Mescheder et al., 2018) . By contrast, the present work formally motivates and justifies the use of averaging for GANs by relating them to the VIP perspective, and sheds light on its underlying intuitions in §3.1. Subsequent to our first preprint, Yazıcı et al. (2019) explored averaging empirically in more depth, while Mertikopoulos et al. (2019) also investigated extrapolation, providing asymptotic convergence results (i.e. without any rate of convergence) in the context of coherent saddle point. The coherence assumption is slightly weaker than monotonicity.

7. EXPERIMENTS:

Our goal in this experimental section is not to provide new state-of-the art results with architectural improvements or a new GAN formulation, but to show that using the techniques (with theoretical guarantees in the monotone case) that we introduced earlier allows us to optimize standard GANs in a better way. These techniques, which are orthogonal to the design of new formulations of GAN optimization objectives, and to architectural choices, can potentially be used for the training of any type of GAN. We will compare the following optimization algorithms: baselines are SGD and Adam using either simultaneous updates on the generator and on the discriminator (denoted SimAdam and SimSGD) or k updates on the discriminator alternating with 1 update on the generator (denoted AltSGD{k} and AltAdam{k}).9 Variants that use extrapolation are denoted ExtraSGD (Alg. 2) and ExtraAdam (Alg. 4). Variants using extrapolation from the past are PastExtraSGD (Alg. 3) and PastExtraAdam (Alg. 4). We also present results using as output the averaged iterates, adding Avg as a prefix of the algorithm name when we use (uniform) averaging. We first test the various stochastic algorithms on a simple (n = 10 3 , d = 10 3 ) finite sum bilinear objective (a monotone operator) constrained to [-1, 1] d :

7.1. BILINEAR SADDLE POINT (STOCHASTIC):

1 n n i=1 θ M (i) ϕ + θ a (i) + ϕ b (i) (29) solved by (θ * , ϕ * ) s.t. M ϕ * = -ā M T θ * = - b , where ā := 1 n n i=1 a (i) , b := 1 n n i=1 b (i) and M := 1 n n i=1 M (i) . The matrices M (i) kj , a (i) k , b (i) k ; 1 ≤ i ≤ n, 1 ≤ j, k ≤ d were randomly generated, but ensuring that (θ * , ϕ * ) belongs to [-1, 1] d . Results

are shown in Fig. 3 . We can see that AvgAltSGD1 and AvgPastExtraSGD perform the best on this task.

7.2. WGAN AND WGAN-GP ON CIFAR10:

We evaluate the proposed techniques in the context of GAN training, which is a challenging stochastic optimization problem where the objectives of both players are non-convex. We propose to evaluate the Adam variants of the different optimization algorithms (see Alg. 4 for Adam with extrapolation) by training two different architectures on the CIFAR10 dataset (Krizhevsky and Hinton, 2009) . First, we consider a constrained zero-sum game by training the DCGAN architecture (Radford et OptimAdam is the related Optimistic Adam (Daskalakis et al., 2018) algorithm. EMA denotes exponential moving average (with β = 0.9999, see Eq. 8). We see that the techniques of extrapolation and averaging consistently enable improvements over the baselines (in italic). Right: WGAN-GP trained on CIFAR10: mean and standard deviation of the inception score computed over 5 runs for each method using the best performing learning rates; all experiments were run on a NVIDIA Quadro GP100 GPU. We see that ExtraAdam converges faster than the Adam baselines.

2016) with the WGAN objective and weight clipping as proposed by Arjovsky et al. (2017) . Then, we compare the different methods on a state-of-the-art architecture by training a ResNet with the WGAN-GP objective similar to Gulrajani et al. (2017) . Models are evaluated using the inception score (IS) (Salimans et al., 2016) computed on 50,000 samples. We also provide the FID (Heusel et al., 2017) and the details on the ResNet architecture in §G.3.

For each algorithm, we did an extensive search over the hyperparameters of Adam. We fixed β 1 = 0.5 and β 2 = 0.9 for all methods as they seemed to perform well. We note that as proposed by Heusel et al. (2017) , it is quite important to set different learning rates for the generator and discriminator. Experiments were run with 5 random seeds for 500,000 updates of the generator.

Tab. 1 reports the best IS achieved on these problems by each considered method. We see that the techniques of extrapolation and averaging consistently enable improvements over the baselines (see §G.5 for more experiments on averaging). Fig. 4 shows training curves for each method (for their best performing learning rate), as well as samples from a ResNet generator trained with ExtraAdam on a WGAN-GP objective. For both tasks, using an extrapolation step and averaging with Adam (ExtraAdam) outperformed all other methods. Combining ExtraAdam with averaging yields results that improve significantly over the previous state-of-the-art IS (8.2) and FID (21.7) on CIFAR10 as reported by Miyato et al. (2018) (see Tab. 5 for FID). We also observed that methods based on extrapolation are less sensitive to learning rate tuning and can be used with higher learning rates with less degradation; see §G.4 for more details.

8. CONCLUSION:

We newly addressed GAN objectives in the framework of variational inequality. We tapped into the optimization literature to provide more principled techniques to optimize such games. We leveraged these techniques to develop practical optimization algorithms suitable for a wide range of GAN training objectives (including non-zero sum games and projections onto constraints). We experimentally verified that this could yield better trained models, improving the previous state of the art. The presented techniques address a fundamental problem in GAN training in a principled way, and are orthogonal to the design of new GAN architectures and objectives. They are thus likely to be widely applicable, and benefit future development of GANs.

A DEFINITIONS:

In this section, we recall usual definitions and lemmas from convex analysis. We start with the definitions and lemmas regarding the projection mapping.

A.1 PROJECTION MAPPING Definition 1. The projection P Ω onto Ω is defined as,

EQUATION

)

When Ω is a convex set, this projection is unique. This is a consequence of the following lemma that we will use in the following sections: the non-expansiveness of the projection onto a convex set. Lemma 1. Let Ω a convex set, the projection mapping P Ω : R d → Ω is nonexpansive, i.e.,

EQUATION

)

This is standard convex analysis result which can be found for instance in (Boyd and Vandenberghe, 2004) . The following lemma is also standard in convex analysis and its proof uses similar arguments as the proof of Lemma 1.

Lemma 2. Let ω ∈ Ω and ω + def = P Ω (ω + u), then for all ω ∈ Ω we have,

EQUATION

)

Proof of Lemma 2. We start by simply developing,

ω + -ω 2 2 = (ω + -ω) + (ω -ω ) 2 2 = ω -ω 2 2 + 2(ω + -ω) (ω -ω ) + ω + -ω 2 2 = ω -ω 2 2 + 2(ω + -ω) (ω + -ω ) -ω + -ω 2 2 .

Then since ω + is the projection onto the convex set Ω of ω + u, we have that (ω + -(ω + u)) (ω + -ω ) ≤ 0 , ∀ ω ∈ Ω, leading to the result of the Lemma.

A.2 SMOOTHNESS AND MONOTONICITY OF THE OPERATOR:

Another important property used is the Lipschitzness of an operator.

EQUATION

In this paper, we also use the notion of strong monotonicity, which is a generalization for operators of the notion of strong convexity. Let us first recall the definition of the latter,

EQUATION

) Definition 4. A function (θ, ϕ) → L(θ, ϕ) is said convex-concave if L(•, ϕ) is convex for all ϕ ∈ Φ and L(θ, •) is concave for all θ ∈ Θ. An L is said to be µ-strongly convex-concave if (θ, ϕ) → L(θ, ϕ) -µ 2 θ 2 2 + µ 2 ϕ 2 2 is convex-concave.

If a function f (resp. L) is strongly convex (resp. strongly convex-concave), its gradient ∇f (resp.

[∇ θ L -∇ ϕ L] ) is strongly monotone, i.e., Definition 5. For µ > 0, an operator F : Ω → R d is said to be µ-strongly monotone if

EQUATION

B GRADIENT METHODS ON UNCONSTRAINED BILINEAR GAMES:

In this section, we will prove the results provided in §3, namely Proposition 1, Proposition 2 and Theorem 1. For Proposition 1 and 2, let us recall the context. We wanted to derive properties of some gradient methods on the following simple illustrative example

min θ∈R max φ∈R θ • φ (36) B.1 PROOF OF PROPOSITION 1

Let us first recall the proposition:

Proposition' 1. The simultaneous iterates diverge geometrically and the alternating iterates defined in (11) are bounded but do not converge to 0 as

Simultaneous: θ 2 t+1 + φ 2 t+1 = (1 + η 2 )(θ 2 t + φ 2 t ) , Alternating: θ 2 t + φ 2 t = Θ(θ 2 0 + φ 2 0 ) (37) where u t = Θ(v t ) ⇔ ∃α, β, t 0 > 0 such that ∀t ≥ t 0 , αv t ≤ u t ≤ βv t .

The uniform average ( θt , φt ) def = 1 t t-1 s=0 (θ s , φ s ) of the simultaneous updates (resp. the alternating updates) diverges (resp. converges to 0) as,

EQUATION

)

Proof. Let us start with the simultaneous update rule:

EQUATION

Then we have,

EQUATION

The update rule (39) also gives us,

EQUATION

Summing (42) for 0 ≤ t ≤ T -1 to get telescoping sums, we get

EQUATION

)

Let us continue with the alternating update rule

EQUATION

Then we have,

EQUATION

By simple linear algebra, for η < 2, the matrix M def = 1 -η η 1 -η 2 has two complex conjugate eigenvalues which are

λ ± = 1 -η η ± i 4 -η 2 2 (48)

and their squared magnitude is equal to det(M ) = 1 -η 2 + η 2 = 1. We can diagonalize M meaning that there exists P an invertible matrix such that M = P -1 diag(λ + , λ -)P . Then, we have

θ t φ t = M t θ 0 φ 0 = P -1 diag(λ t + , λ t -)P θ 0 φ 0 (49)

and consequently,

EQUATION

where • C is the norm in C 2 and P := max u∈C 2 P u C u C is the induced matrix norm. The same way we have,

EQUATION

Hence, if θ 2 0 + φ 2 0 > 0, the sequence (θ t , φ t ) is bounded but do not converge to 0. Moreover the update rule gives us,

EQUATION

Consequently, since

EQUATION

B.2 IMPLICIT AND EXTRAPOLATION METHOD:

In this section, we will prove a slightly more precise proposition than Proposition 2, Proposition' 2. The squared norm of the iterates N 2 t def = θ 2 t + φ 2 t , where the update rule of θ t and φ t is defined in (18), decrease geometrically for any 0 < η < 1 as,10 Implicit:

N 2 t+1 = N 2 t 1 + η 2 , Extrapolation: N 2 t+1 = (1 -η 2 + η 4 )N 2 t , ∀t ≥ 0 (54)

Proof. Let us recall the update rule for the implicit method

EQUATION

Then,

EQUATION

implying that

EQUATION

)

which is valid for any η.

For the extrapolation method, we have the update rule

EQUATION

Implying that,

EQUATION

B.3 GENERALIZATION TO GENERAL UNCONSTRAINED BILINEAR OBJECTIVE:

In this section, we will show how to simply extend the study of the algorithm of interest provided in §3 on the general unconstrained bilinear example,

min θ∈R d max ϕ∈R p θ Aϕ -b θ -c ϕ (63)

where, A ∈ R d×p , b ∈ R d and c ∈ R p . The only assumption we will make is that this problem is feasible which is equivalent to say that there exists a solution (θ * , ϕ * ) to the system

EQUATION

In this case, we can re-write (63) as

EQUATION

where c := -θ * Aϕ * is a constant that does not depend on θ and ϕ.

First, let us show that we can reduce the study of simultaneous, alternating, extrapolation and implicit updates rules for (63) to the study of the respective unidimensional updates (11) and ( 18).

This reduction has already been proposed by Gidel et al. (2019) . For completeness, we reproduce here similar arguments. The following lemma is a bit more general than the result provided by Gidel et al. (2019) . It states that the study of a wide class of unconstrained first order method on (63) can be reduced to the study of the method on (36), with potentially rescaled step-sizes.

Before explicitly stating the lemma, we need to introduce a bit of notation to encompass easily our several methods in a unified way. First, we let ω t := (θ t , ϕ t ), where the index t here is a more general index which can vary more often than the one in §3. For example, for the extrapolation method, we could consider ω 1 = ω 0+1/2 and ω 2 = ω 1 , where ω was the sequence defined for the extragradient. For the alternated updates, we can consider ω 1 = (θ 1 , ϕ 0 ) and ω 2 = (θ 1 , ϕ 1 ) (this also defines θ 2 = θ 1 ), where θ and ϕ were the sequences originally defined for alternated updates. We are thus ready to state the lemma.

Lemma 3. Let us consider the following very general class of first order methods on (63), i.e.,

EQUATION

where ω t := (θ t , ϕ t ) and F θ (ω t ) := Aϕ t -b, F ϕ (ω t ) = A θ t -c. Then, we have

EQUATION

where A = U DV (SVD decomposition) and the couples ([ θt ] i , [ φ] i ) 1≤i≤r follow the update rule of the same method on a unidimensional problem (36). In particular, for our methods of interest, the couples ([ θt ] i , [ φ] i ) 1≤i≤r follow the same update rule with a respective step-size σ i η, where σ i are the singular values on the diagonal of D.

Proof. Our general class of first order methods can be written with the following update rules:

θ t+1 = θ 0 + t+1 s=0 λ st A(ϕ s -ϕ * ) ϕ t+1 = ϕ 0 + t+1 s=0 µ st A (θ s -θ * ) ,

where λ it , µ it ∈ R , 0 ≤ i ≤ t + 1. We allow the dependence on t for the algorithm coefficients λ and µ (for example, the alternating rule would zero out some of the coefficients depending on whether we are updating θ or ϕ at the current iteration). Notice also that if both λ (t+1)t and µ (t+1)t are non-zero, we have an implicit scheme.

Thus, using the SVD of A = U DV , we get

EQUATION

)

where D is a rectangular matrix with zeros except on a diagonal block of size r. Thus, each coordinate of θt+1 and φt+1 are updated independently, reducing the initial problem to r unidimensional problems,

EQUATION

where σ 1 ≥ . . . ≥ σ r > 0 are the positive diagonal coefficients of D.

Finally, for the coordinate i where the diagonal coefficient of D is equal to 0, we can notice that the sequence ([ θt ] i , [ φt ] i ) is constant. Moreover, we have the freedom to chose any [θ * ] i ∈ R and [ϕ * ] i ∈ R as a coordinate of the solution of (63). We thus set them respectively equal to [θ 0 ] i and [ϕ 0 ] i . The update rule (69) corresponds to the update rule of the general first order method considered on this proof on the unidimensional problem (36).

Note that the only additional restriction is that the coefficients (λ st ) and (σ st ) (that are the same for 1 ≤ i ≤ r) are rescaled by the singular values of A. In practice, for our methods of interest with a step-size η, it corresponds to the study of r unidimensional problem with a respective step-size

σ i η , 1 ≤ i ≤ r.

From this lemma, an extension of Proposition 1 and 2 directly follows to the general unconstrained bilinear objective (63). We note

EQUATION

)

where (Θ * , Φ * ) is the set of solutions of (63). The following corollary is divided in two points, the first point is a result from Gidel et al. (2019) (note that the result on the average is a straightforward extension of the one provided in Proposition 1 and was not provided by Gidel et al. (2019) ), the second result is new. Very similar asymptotic upper bounds regarding extrapolation and implicit methods can be derived by Tseng (1995) computing the exact values of the constant τ 1 and τ 2 (and noticing that τ 3 = ∞) introduced in (Tseng, 1995, Eq. 3 & 4) for the unconstrained bilinear case. However, since Tseng (1995) works in a very general setting, the bound are not as tight as ours and his proof technique is a bit more technical. Our reduction above provides here a simple proof for our simple setting.

Corollary 1.

• Gidel et al. (2019) : The simultaneous iterates diverge geometrically and the alternating iterates are bounded but do not converge to 0 as, Simultaneous: 71) where u t = Θ(v t ) ⇔ ∃α, β, t 0 > 0 such that ∀t ≥ t 0 , αv t ≤ u t ≤ βv t . The uniform average ( θt , φt ) def = 1 t t-1 s=0 (θ s , φ s ) of the simultaneous updates (resp. the alternating updates) diverges (resp. converges to 0) as,

N 2 t+1 = (1 + (σ min (A)η) 2 )N 2 t , Alternating: N 2 t = Θ(N 2 0 ) , (

Simultaneous: N 2 t ≤ Θ N 2 0 t 2 (1 + (σ min (A)η) 2 ) t , Alternating: N 2 t = Θ N 2 0 t 2 .

• Extrapolation and Implicit method: The iterates respectively generated by the update rules ( 14) and ( 17) on a bilinear unconstrained problem (63) do converge linearly for any

EQUATION

)

Particularly, for η = 1 2σmax(A) we get for the extrapolation method, Extrapolation:

EQUATION

)

where κ := σmax(A) 2 σmin(A) 2 is the condition number of A A.

B.4 EXTRAPOLATION FROM THE PAST FOR STRONGLY CONVEX OBJECTIVES:

Let us recall what we call projected extrapolation form the past, where we used the notation ω t = ω t+1/2 for compactness, Extrapolation from the past:

EQUATION

Perform update step: ω t+1 = P Ω [ω t -ηF (ω t )] and store:

EQUATION

where P Ω [•] is the projection onto the constraint set Ω. An operator F : Ω → R d is said to be µ-strongly monotone if

EQUATION

)

If F is strongly monotone, we can prove the following theorem:

Theorem' 1. If F is µ-strongly monotone (see §A for the definition of strong monotonicity) and L-Lipschitz, then the updates (20) and ( 21) with η = 1 4L provide linearly converging iterates,

EQUATION

)

Proof. In order to prove this theorem, we will prove a slightly more general result,

EQUATION

)

with the convention that ω

EQUATION

)

Let us first proof three technical lemmas.

Lemma 4. If F is µ-strongly monotone, we have

EQUATION

)

Proof. By strong monotonicity and optimality of ω * ,

2µ ω t -ω * 2 2 ≤ 2F (ω * ) (ω t -ω * ) + 2µ ω t -ω * 2 2 ≤ 2F (ω t ) (ω t -ω * ) (82)

and then we use the inequality

EQUATION

)

Proof. Applying Lemma 2 for (ω, u, ω

+ , ω ) = (ω t , -η t F (ω t ), ω t+1 , ω) and (ω, u, ω + , ω ) = (ω t , -η t F (ω t-1 ), ω t , ω t+1 ), we get, ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t ) (ω t+1 -ω) -ω t+1 -ω t 2 2 (84) and ω t -ω t+1 2 2 ≤ ω t -ω t+1 2 2 -2η t F (ω t-1 ) (ω t -ω t+1 ) -ω t -ω t 2 2 .

(85) Summing ( 84) and ( 85) we get,

EQUATION

Then, we can use the Young's inequality 2a b ≤ a 2 2 + b 2 2 to get,

EQUATION

)

Lemma 6. For all t ≥ 0, if we set ω -2 = ω -1 = ω 0 we have

EQUATION

Proof. We start with a

EQUATION

)

Moreover, since the projection is contractive we have that

EQUATION

Combining ( 92) and (95) we get,

EQUATION

)

Proof of Theorem 1. Let ω * ∈ Ω * be an optimal point of (VIP). Combining Lemma 4 and Lemma 5 we get,

η t µ ω t -ω * 2 2 -2 ω t -ω t 2 2 ≤ ω t -ω * 2 2 -ω t+1 -ω * 2 2 +η 2 t L 2 ω t-1 -ω t 2 2 -ω t -ω t 2 2 leading to, ω t+1 -ω * 2 2 ≤ (1 -η t µ) ω t -ω * 2 2 + η 2 t L 2 ω t-1 -ω t 2 2 -(1 -2η t µ) ω t -ω t 2 2

(99)

Now using Lemma 6 we get,

EQUATION

Now with η t = 1 4L ≤ 1 4µ we get,

ω t+1 -ω * 2 2 ≤ 1 - µ 4L ω t -ω * 2 2 + 1 16 1 4 ω t-1 -ω t-2 2 2 -ω t-1 -ω t 2 2

Hence, using the fact that µ 4L ≤ 1 4 we get,

EQUATION

C MORE ON MERIT FUNCTIONS:

In this section, we will present how to handle an unbounded constraint set Ω with a more refined merit function than (25) used in the main paper. Let F be the continuous operator and Ω be the constraint set associated with the VIP,

find ω * ∈ Ω such that F (ω * ) (ω -ω * ) ≥ 0 , ∀ω ∈ Ω . (VIP)

When the operator F is monotone, we have that

F (ω * ) (ω -ω * ) ≤ F (ω) (ω -ω * ) , ∀ω, ω * .

Hence, in this case (VIP) implies a stronger formulation sometimes called Minty variational inequality (Crespi et al., 2005) :

find ω * ∈ Ω such that F (ω) (ω -ω * ) ≥ 0 , ∀ω ∈ Ω . (MVI)

This formulation is stronger in the sense that if (MVI) holds for some ω * ∈ Ω, then (VIP) holds too. A merit function useful for our analysis can be derived from this formulation. Roughly, a merit function is a convergence measure. More formally, a function g : Larsson and Patriksson, 1994) . A way to derive a merit function from (MVI) would be to use g(ω * ) = sup ω∈Ω F (ω) (ω * -ω) which is zero if and only if (MVI) holds for ω * . To deal with unbounded constraint sets (leading to a potentially infinite valued function outside of the optimal set), we use the restricted merit function (Nesterov, 2007) :

Ω → R is called a merit function if g is non-negative such that g(ω) = 0 ⇔ ω ∈ Ω * (

EQUATION

This function acts as merit function for (VIP) on the interior of the open ball of radius R around ω 0 , as shown in Lemma 1 of Nesterov (2007) . That is, let

EQUATION

The reference point ω 0 is arbitrary, but in practice it is usually the initialization point of the algorithm. R has to be big enough to ensure that Ω R contains a solution. Err R measures how much (MVI) is violated on the restriction Ω R . Such merit function is standard in the variational inequality literature.

A similar one is used in (Nemirovski, 2004; Juditsky et al., 2011) . When F is derived from the gradients (5) of a zero-sum game, we can define a more interpretable merit function. One has to be careful though when extending properties from the minimization setting to the saddle point setting (e.g. the merit function used by Yadav et al. (2018) is vacuous for a bilinear game as explained in App C.2).

In the appendix, we adopt a set of assumptions a little more general than the one in the main paper:

A less restrictive way to consider variational inequalities in the non-monotone setting is to use a local version of (MVI). If the cost functions are locally convex around the optimal couple (θ * , ϕ * ) and if our iterates eventually fall and stay into that neighborhood, then we can consider our restricted merit function Err R (•) with a well suited constant R and apply our convergence results for monotone operators.

D ANOTHER WAY OF IMPLEMENTING EXTRAPOLATION TO SGD:

We now introduce another way to combine extrapolation and SGD. This extension is very similar to AvgExtraSGD Alg. 2, the only difference is that it re-uses the mini-batch sample of the extrapolation step for the update of the current point. The intuition is that it correlates the estimator of the gradient of the extrapolation step and the one of the update step leading to a better correction of the oscillations which are also due to the stochasticity. One emerging issue (for the analysis) of this method is that since ω t depend on ξ t , the quantity F (ω t , ξ t ) is a biased estimator of F (ω t ).

Algorithm 5 Re-used mini-batches for stochastic extrapolation (ReExtraSGD)

1: Let ω 0 ∈ Ω 2: for t = 0 . . . T -1 do 3: Sample ξ t ∼ P 4: ω t def = P Ω [ω t -η t F (ω t , ξ t )] Extrapolation step 5: ω t+1 def = P Ω [ω t -η t F ω t , ξ t ]

Update step with the same sample 6: end for

7: Return ωT = T -1 t=0 η t ω t / T -1 t=0 η t

Theorem 5. Assume that ω t -ω 0 ≤ R, ∀t ≥ 0 where (ω t ) t≥0 are the iterates of Alg. 5. Under Assumption 1 and 4, for any T ≥ 1, Alg. 5 with constant step-size η ≤ 1 √ 2L has the following convergence properties:

E[Err R ( ωT )] ≤ R 2 ηT + η σ 2 + 4L 2 (4R 2 + σ 2 ) 2 where ωT def = 1 T T -1 t=0 ω t .

Particularly,

η t = η √ T gives E[Err R ( ωT )] ≤ O(1) √ T .

The assumption that the sequence of the iterates provided by the algorithm is bounded is strong, but has also been made for instance in (Yadav et al., 2018) . The proof of this result is provided in §F.

E VARIANCE COMPARISON BETWEEN AVGSGD AND SGD WITH PREDICTION:



METHOD:

To compare the variance term of AvgSGD in (26) with the one of the SGD with prediction method (Yadav et al., 2018), we need to have the same convergence certificate. Fortunately, their proof can be adapted to our convergence criterion (using Lemma 7 in §F), revealing an extra σ 2 /2 in the variance term from their paper. The resulting variance can be summarized with our notation as (M 2 (1 + L) + σ 2 )/2 where the L is the Lipschitz constant of the operator F . Since M σ, their variance term is then 1 + L time larger than the one provided by the AvgSGD method.

F PROOF OF THEOREMS:

This section is dedicated on the proof of the theorems provided in this paper in a slightly more general form working with the merit function defined in (108). First we prove an additional lemma necessary to the proof of our theorems.

Lemma 7. Let F be a monotone operator and let (ω t ), (ω t ), (z t ), (∆ t ), (ξ t ) and (ζ t ) be six random sequences such that, for all t ≥ 0 2η

t F (ω t ) (ω t -ω) ≤ N t -N t+1 + η 2 t (M 1 (ω t , ξ t ) + M 2 (ω t , ζ t )) + 2η t ∆ t (z t -ω) ,

where N t = N (ω t , ω t-1 , ω t-2 ) ≥ 0 and we extend (ω t ) with ω -2 = ω -1 = ω 0 . Let also assume that with

EQUATION

)

where

ωT def = T -1 t=0 η t ω t /S T and S T def = T -1 t=0 η t .

Proof of Lemma 7. We sum (7) for 0 ≤ t ≤ T -1 to get,

EQUATION

)

We will then upper bound each sum in the right-hand side,

∆ t (z t -ω) = ∆ t (z t -u t ) + ∆ t (u t -ω)

where u t+1 def = P Ω (u t -η t ∆ t ) and u 0 def = ω 0 . Then,

EQUATION

Then noticing that z 0 def = ω 0 , back to (110) we get a telescoping sum,

EQUATION

2 )+2η t ∆ t (z t -u t ) .

(112) If F is the operator of a convex-concave saddle point (5), we get, with ω t = (θ t , ϕ t )

F (ω t ) (ω t -ω) ≥ ∇ θ L(θ t , ϕ t ) (θ t -θ) -∇ ϕ L(θ t , ϕ t ) (ϕ t -ϕ)

≥ L(θ t , ϕ) -L(θ t , ϕ t ) + L(θ t , ϕ t ) -L(θ, ϕ t ) (by convexity and concavity) = L(θ t , ϕ) -L(θ, ϕ t ) then by convexity of L(•, ϕ) and concavity of L(θ, •), we have that,

2S T T -1 t=0 η t S T F (ω t ) (ω t -ω) ≥ 2S T T -1 t=0 η t S T (L(θ t , ϕ) -L(θ, ϕ t )) ≥ 2S T (L( θt , ϕ) -L( θ, ϕ t )) (113) Otherwise if the operator F is just monotone since F (ω t ) (ω t -ω) ≥ F (ω ) (ω t -ω) we have that 2S T T -1 t=0 η t F (ω t ) (ω t -ω) ≥ 2S T T -1 t=0 η t F (ω ) (ω t -ω) = 2S T F (ω ) ( ωt -ω) (114)

In both cases, we can now maximize the left hand side respect to ω (since the RHS does not depend on ω) to get,

EQUATION

)

Then taking the expectation, since

E[∆ t |z t , u t ] = E[∆ t |z t , ∆ 0 , . . . , ∆ t-1 ] = 0, E ζt [ ∆ t 2 2 ] ≤ σ 2 , E ξt [M 1 (ω t , ξ t )] ≤ M 1 and E ζt [M 2 (ω t , ζ t )] ≤ M 2 , we get that, E[Err R ( ωT )] ≤ R 2 S T + M 1 + M 2 + σ 2 2S T T -1 t=0 η 2 t (116)

F.1 PROOF OF THM. 2

First let us state Theorem 2 in its general form, Theorem' 2. Under Assumption 1, 2 and 4, Alg. 1 with constant step-size η has the following convergence rate for all T ≥ 1,

EQUATION

)

Particularly, η = R √ T (M 2 +σ 2 ) gives E[Err R ( ωT )] ≤ R √ M 2 +σ 2 √ T . Proof of Theorem 2. Let any ω ∈ Ω such that ω 0 -ω 2 ≤ R, ω t+1 -ω 2 2 = P Ω (ω t -η t F (ω t , ξ t )) -ω 2 2 ≤ ω t -η t F (ω t , ξ t )) -ω 2 2

(projections are non-contractive, Lemma 1)

= ω t -ω 2 2 -2η t F (ω t , ξ t ) (ω t -ω) + η t F (ω t , ξ t ) 2 2

Then we can make appear the quantity F (ω t ) (ω t -ω) on the left-hand side,

2η t F (ω t ) (ω t -ω) ≤ ω t -ω 2 2 -ω t+1 -ω 2 2 +η 2 t F (ω t , ξ t ) 2 2 +2η t (F (ω t )-F (ω t , ξ t )) (ω t -ω) (118) we can sum (118) for 0 ≤ t ≤ T -1 to get, 2 T -1 t=0 η t F (ω t ) (ω t -ω) ≤ T -1 t=0 ( ω t -ω 2 -ω t+1 -ω 2 ) + η 2 t F (ω t , ξ t ) 2 2 + 2η t ∆ t (ω t -ω) (119)

where we noted ∆ t def = F (ω t ) -F (ω t , ξ t ). By monotonicity, F (ω t ) (ω t -ω) ≥ F (ω) (ω t -ω) we get, T -1 t=0 η t ω t . We will then upper bound each sum in the right hand side,

EQUATION

∆ t (ω t -ω) = ∆ t (ω t -u t ) + ∆ t (u t -ω)

where u t+1 def = P Ω (u t -η t ∆ t ) and u 0 = ω 0 . Then,

u t+1 -ω 2 2 ≤ u t -ω 2 2 -2η t ∆ t (u t -ω) + η 2 t ∆ t 2 2

leading to

2η t ∆ t (ω t -ω) ≤ 2η t ∆ t (ω t -u t ) + u t -ω 2 2 -u t+1 -ω 2 2 + η 2 t ∆ t 2 2 (121)

Then noticing that u 0 def = ω 0 , back to (120) we get a telescoping sum,

2S T F (ω) ( ωT -ω) ≤ 2 ω 0 -ω 2 + T -1 t=0 η 2 t ( F (ω t , ξ t ) 2 2 + ∆ t 2 2 ) + 2 T -1 t=0 η t ∆ t (ω t -u t ) ≤ 2R + T -1 t=0 η 2 t ( F (ω t , ξ t ) 2 2 + ∆ t 2 2 ) + 2 T -1 t=0 η t ∆ t (ω t -u t )

Then the right hand side does not depends on ω, we can maximize over ω to get,

EQUATION

Noticing that E[∆ t |ω t , u t ] = 0 (the estimates of F are unbiased), by Assumption 2

EQUATION

)

particularly for η t = η and η t = η √ t+1 we respectively get,

EQUATION

)

and

EQUATION

F.2 PROOF OF THM. 3

Theorem' 3. Under Assumption 1 and 4, if E ξ [F ] is L-Lipschitz, then Alg. 2 with a constant step-size η ≤ 1 √ 3L has the following convergence rate for any T ≥ 1,

EQUATION

Particularly, η =

√ 2R σ √ 7T gives E[Err R ( ωT )] ≤ √ 14Rσ √ T .

Proof of Thm. 3. Let any ω ∈ Ω such that ω 0 -ω 2 ≤ R. Then, the update rules become ω t+1 = P Ω (ω t -η t F (ω t , ζ t )) and ω t = P Ω (ω t -ηF (ω t , ξ t )). We start by applying Lemma 2 for (ω, u, ω , ω + ) = (ω t , -ηF (ω t , ζ t ), ω, ω t+1 ) and (ω, u, ω , ω + ) = (ω t , -η t F (ω t , ξ t ), ω t+1 , ω t ),

ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ζ t ) (ω t+1 -ω) -ω t+1 -ω t 2 2 ω t -ω t+1 2 2 ≤ ω t -ω t+1 2 2 -2η t F (ω t , ξ t ) (ω t -ω t+1 ) -ω t -ω t 2 2

Then, summing them we get

ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ζ t ) (ω t+1 -ω) -2η t F (ω t , ξ t ) (ω t -ω t+1 ) -ω t -ω t 2 2 -ω t+1 -ω t 2 2 (127) leading to ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ζ t ) (ω t -ω) + 2η t (F (ω t , ζ t ) -F (ω t , ξ t )) (ω t -ω t+1 ) -ω t -ω t 2 2 -ω t+1 -ω t 2 2 Then with 2a b ≤ a 2 2 + b 2 2 we get ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ζ t ) (ω t -ω) -ω t -ω t 2 2 + η 2 t F (ω t , ζ t ) -F (ω t , ξ t ) 2 2 Using the inequality a + b + c 2 2 ≤ 3( a 2 2 + b 2 2 + c 2 2 ) we get, ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ζ t ) (ω t -ω) -ω t -ω t 2 2 + 3η 2 t ( F (ω t ) -F (ω t , ξ t ) 2 2 + F (ω t ) -F (ω t , ζ t ) 2 2 + F (ω t ) -F (ω t ) 2 2 )

Then we can use the L-Lipschitzness of F to get,

EQUATION

2 )

As we restricted the step-size to η t ≤ 1 √ 3L we get,

2η t F (ω t ) (ω t -ω) ≤ ω t -ω 2 2 -ω t+1 -ω 2 2 + 2η t (F (ω t ) -F (ω t , ζ t )) (ω t -ω) + 3η 2 t F (ω t ) -F (ω t , ξ t ) 2 2 + 3η 2 t F (ω t ) -F (ω t , ζ t ) 2 2

We get a particular case of (7) so we can use Lemma 7 where

N t = ω t -ω 2 2 , M 1 (ω t , ξ t ) = 3 F (ω t )-F (ω t , ξ t ) 2 2 , M 2 (ω t , ζ t ) = 3 F (ω t )-F (ω t , ζ t ) 2 2 , ∆ t = F (ω t )-F (ω t , ζ t ) and z t = ω t .

By Assumption 1, M 1 = M 2 = 3σ 2 and by the fact that

E[F (ω t ) -F (ω t , ζ t ) |ω t , ∆ 0 , . . . , ∆ t-1 ] = E[E[F (ω t ) -F (ω t , ζ t ) |ω t ]|∆ 0 , . . . , ∆ t-1 ] = 0

the hypothesis of Lemma 7 hold and we get,

E[Err R ( ωT )] ≤ R 2 S T + 7σ 2 2S T T -1 t=0 η 2 t (128) F.3 PROOF OF THM. 4 Theorem' 4. Under Assumption 1, if E ξ [F ] is L-Lipschitz, then AvgPastExtraSGD (Alg. 3) with a constant step-size η ≤ 1 2 √

3L has the following convergence rate for any T ≥ 1,

EQUATION

Particularly

, η = √ 2R σ √ 13T gives E[Err R ( ωT )] ≤ √ 26Rσ √ T .

First let us recall the update rule

EQUATION

Lemma 8. We have for any ω ∈ Ω,

EQUATION

and

EQUATION

Summing ( 132) and ( 133) we get,

EQUATION

Then, we can use the inequality of arithmetic and geometric means 2a b ≤ a

EQUATION

)

Using the inequality a

EQUATION

where we used the L-Lipschitzness of F for the last inequality.

Combining ( 140) with ( 142) we get,

EQUATION

EQUATION

Proof. We start with a

EQUATION

)

Moreover, since the projection is contractive we have that

EQUATION

where in the last line we used the same inequality as in (142). Combining ( 144) and ( 148) we get,

EQUATION

)

Proof of Theorem 4. Combining Lemma 9 and Lemma 8 we get,

EQUATION

Then for η

EQUATION

We can then use Lemma 7 where

N t = ω t -ω 2 2 + 3L 3 η t-1 ω t-1 -ω t-2 2 2 , M 1 (ω t , ξ t ) = 0 M 2 (ω t , ξ t ) = 3 F (ω t ) -F (ω t , ξ t ) 2 2 + 6 F (ω t-1 ) -F (ω t-1 , ξ t-1 ) 2 2 + 3 F (ω t-2 ) -F (ω t-2 , ξ t-2 ) 2 2 ∆ t = F (ω t ) -F (ω t , ξ t ) z t = ω t . By Assumption 1, M 2 = 12σ 2 and by the fact that E[F (ω t ) -F (ω t , ξ t ) |ω t , ∆ 0 , . . . , ∆ t-1 ] = E[E[F (ω t ) -F (ω t , ξ t ) |ω t ]|∆ 0 , . . . , ∆ t-1 ] = 0

the hypothesis of Lemma 7 hold and we get,

EQUATION

F.4 PROOF OF THEOREM 5

Theorem 5 has been introduced in §D. This theorem is about Algorithm 5 which consists in another way to implement extrapolation to SGD. Let us first restate this theorem, Theorem' 5. Assume that ω t -ω 0 ≤ R, ∀t ≥ 0 where (ω t ) t≥0 are the iterates of Alg. 5. Under Assumption 1 and 4, for any T ≥ 1, Alg. 5 with constant step-size η ≤ 1 √ 2L has the following convergence properties:

E[Err R ( ωT )] ≤ R 2 ηT + η σ 2 + 4L 2 (4R 2 + σ 2 ) 2 where ωT def = 1 T T -1 t=0 ω t .

Particularly, η t = η √ T gives E[Err R ( ωT )] ≤ O(1) √ T .

Proof of Thm. 5. Let any ω ∈ Ω such that ω 0 -ω 2 ≤ R. Then, the update rules become ω t+1 = P Ω (ω t -η t F (ω t , ξ t )) and ω t = P Ω (ω t -ηF (ω t , ξ t )). We start the same way as the proof of Thm. 3 by applying Lemma 2 for (ω, u, ω , ω * ) = (ω t , -ηF (ω t , ξ t ), ω, ω t+1 ) and (ω, u, ω , ω + ) = (ω t , -η t F (ω t , ξ t ), ω t+1 , ω t ),

ω t+1 -ω 2 2 ≤ ω t -ω 2 2 -2η t F (ω t , ξ t ) (ω t+1 -ω) -ω t+1 -ω t G ADDITIONAL EXPERIMENTAL RESULTS

G.1 TOY NON-CONVEX GAN (2D AND DETERMINISTIC):

We now consider a task similar to (Mescheder et al., 2018) where the discriminator is linear D ϕ (ω) = ϕ T ω, the generator is a Dirac distribution at θ, q θ = δ θ and the distribution we try to match is also a Dirac at ω * , p = δ ω * . The minimax formulation from Goodfellow et al. (2014) gives: min θ max ϕ -log 1 + e -ϕ T ω * -log 1 + e ϕ T θ (158) Note that as observed by Nagarajan and Kolter (2017) , this objective is concave-concave, making it hard to optimize. We compare the methods on this objective where we take ω * = -2, thus the position of the equilibrium is shifted towards the position (θ, ϕ) = (-2, 0). The convergence and the gradient vector field are shown in Figure 5 . We observe that depending on the initialization, some methods can fail to converge but extrapolation (18) seems to perform better than the other methods. 

G.2 DCGAN WITH WGAN-GP OBJECTIVE:

In addition to the results presented in section §7.2, we also trained the DCGAN architecture with the WGAN-GP objective. The results are shown in Table 3 . The best results are achieved with uniform averaging of AltAdam5. However, its iterations require to update the discriminator 5 times for every generator update. With a small drop in best final score, ExtraAdam can train WGAN-GP significantly faster (see Fig. 6 right) as the discriminator and generator are updated only twice.

G.3 FID SCORES FOR RESNET ARCHITECTURE WITH WGAN-GP OBJECTIVE:

In addition to the inception scores, we also computed the FID scores (Heusel et al., 2017) using 50,000 samples for the ResNet architecture with the WGAN-GP objective; the results are presented in Table 5 . We see that the results and conclusions are similar to the one obtained from the inception scores, adding an extrapolation step as well as using Exponential Moving Average (EMA) consistently improves the FID scores. However, contrary to the results from the inception score, we observe that uniform averaging does not necessarily improve the performance of the methods. This could be due to the fact that the samples produced using uniform averaging are more blurry and FID is more sensitive to blurriness; see §G.3 for more details about the effects of uniform averaging. 

G.5 COMPARISON OF THE METHODS WITH AND WITHOUT UNIFORM AVERAGING:

In this section, we compare how uniform averaging affect the performance of the methods presented in §7. We follow the same protocol as in the experimental section §7, we consider the DCGAN architecture with the WGAN and weight clipping objective as well as the WGAN-GP objective. In Figure 9 and 10, we plot the inception score provided by each training method as a function of the number of generator updates with and without uniform averaging.

We notice that uniform averaging seems to improve the inception score, nevertheless it looks like the sample are a bit more blurry (see Figure 11 ). This is confirmed by our result (Figure 12 ) on the Fréchet Inception Distance (FID) which is more sensitive to blurriness. A similar observation about FID was made in §G.3. 2017) computed using 50,000 samples, on the WGAN experiments. ReExtraAdam refers to Alg. 5 introduced in §D. We can see that averaging performs worse than when comparing with the Inception Score. We observed that the samples generated by using averaging are a little more blurry and that the FID is more sensitive to blurriness, thus providing an explanation for this observation.

:

The preprints for(Mertikopoulos et al.,

2019) and(Yazıcı et al., 2019), which respectively explored extrapolation and averaging for GANs, appeared after our initial preprint. See also the related work section §6.

Code available at https://gauthiergidel.github.io/projects/vip-gan.html.

An example of constraint for GANs is to clip the parameters of the discriminator(Arjovsky et al., 2017).

Strong monotonicity, a generalization of strong convexity. See §A.

Wasserstein GAN (WGAN) proposed by Arjovsky et al. (2017) boils down to the following minimax formulation: min θ∈Θ max ϕ∈Φ,||Dϕ|| L ≤1 Ex∼p[Dϕ(x)] -E x ∼q θ [Dϕ(x )].

Sutskever (2013, §7.2) showed the equivalence between "standard momentum" and Nesterov's formulation.

The convexity of the cost functions in (3) is a necessary condition (not sufficient) for the operator to be monotone. In the context of a zero-sum game, the convexity of the cost functions is a sufficient condition.

The gradient penalty is only added to the discriminator cost function. Since this gradient penalty depends also on the generator, WGAN-GP cannot be cast as a SP problem and is actually a non-zero sum game.

In the original WGAN paper(Arjovsky et al., 2017), the authors use k = 5.

Note that the relationship (54) holds actually for any η for the implicit method, and thus the decrease is geometric for any non-zero step size.

As before, the inequality (73) for the implicit scheme is actually valid for any step-size.

Figures and Tables:

ϕ) where L(θ, ϕ) def = -E x∼p [log D ϕ (x)] -E x ∼q θ [log(1 -D ϕ (x ))] .

Figure 1: Comparison of the basic gradient method (as well as

Figure3: Performance of the considered stochastic optimization algorithms on the bilinear problem (29). Each method uses its respective optimal step-size found by grid-search.

Figure 4: Left: Mean and standard deviation of the inception score computed over 5 runs for each method on WGAN trained on CIFAR10. To keep the graph readable we show only SimAdam but AltAdam performs similarly. Middle: Samples from a ResNet generator trained with the WGAN-GP objective using AvgExtraAdam.Right: WGAN-GP trained on CIFAR10: mean and standard deviation of the inception score computed over 5 runs for each method using the best performing learning rates; all experiments were run on a NVIDIA Quadro GP100 GPU. We see that ExtraAdam converges faster than the Adam baselines.

Figure5: Comparison of five algorithms (described in Section 3) on the non-convex GAN objective (158), using the optimal step-size for each method. Left: The gradient vector field and the dynamics of the different methods. Right:The distance to the optimum as a function of the number of iterations.

Figure 8: Comparison of the samples quality on the WGAN-GP (DCGAN) experiment for different methods and learning rate η.

γ = 1 • 10 -4 SimAdam γ = 1 • 10 -4 PastExtraAdam γ = 1 • 10 -4 ExtraAdam γ = 1 • 10 -4 γ = 1 • 10 -4 SimAdam γ = 1 • 10 -4 PastExtraAdam γ = 1 • 10 -4 ExtraAdam γ = 1 • 10 -4(b) without averaging

Figure9: Inception Score on CIFAR10 for WGAN over number of generator updates with and without averaging. We can see that averaging improve the inception score.

al., Best inception scores (averaged over 5 runs) achieved on CIFAR10 for every considered Adam variant.

Table content: <table><tr><td>Method</td><td>no avg</td><td>uniform avg</td><td>EMA</td><td>no avg</td><td>uniform avg</td><td>EMA</td></tr></table>

Peer review (iclr2019):
 Summary: The authors take a variational inequality perspective to the study of the saddle point problem that defines a GAN. By doing so, they are able to profit from the corresponding literature and propose a few methods that are variants of SGD. The authors show in a simple example (a bilinear function) these exhibit better performance than Adam and a basic gradient method. After showing theoretical guarantees of these methods (linear convergence) the authors propose to combine them with existing techniques, and show in fact this leads to better results. Evaluation This is a very good paper and I cannot but recommend its acceptance: It is clear and well written. It has the right level of balance between theory and experiments. Theoretical results are far from trivial. I haven*t seen something similar. The authors*s do not make overstatements: they do not claim to have solved the GAN problem, but they do report improvements which are due to a thorough analysis (see above points). These results are much appreciated.