Cooperative Adversarial Learning via Closed-Loop Transcription
Authors: Rui Xiao
Xinyu Zhao
Venue: Submitted to ICLR 2023 (2023)
Keywords: generative models
rate reduction
closed-loop transcription

Abstract: This paper proposes a generative model that implements cooperative adversarial learning via closed-loop transcription. In the generative model training, the encoder and decoder are trained simultaneously, and not only the adversarial process but also a cooperative process is included. In the adversarial process, the encoder plays as a critic to maximize the distance between the original and transcribed images, in which the distance is measured by rate reduction in the feature space; in the cooperative process, the encoder and the decoder cooperatively minimize the distance to improve the transcription quality. Cooperative adversarial learning possesses the concepts and properties of Auto-Encoding and GAN, and it is unique in that the encoder actively controls the training process as it is trained in both learning processes in two different roles. Experiments demonstrate that without regularization techniques, our generative model is robust to net architectures and easy to train, sample-wise reconstruction performs well in terms of sample features, and disentangled visual attributes are well modeled in independent principal components.

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COOPERATIVE ADVERSARIAL LEARNING VIA BLOSED-LOOD TRANSCRIPTION
Anonymous authors Paper under double-blind review
ABSTRACT
Generative models based on the adversarial process are sensitive to net architec- tures and difficult to train. This paper proposes a generative model that imple- ments cooperative adversarial learning via closed-loop transcription. In the gen- erative model training, the encoder and decoder are trained simultancously, and not only the adversarial process but also a cooperative process is included. the adversarial process, the encoder plays between the original and transcribed images, in which the distance is measured by rate reduction in the feature space; in the cooperative process, the encoder and the decoder cooperatively minimize the distance to improve the transcription quality. Cooperative adversarial learning possesses the concepts and properties of Auto-Encoding and GAN, and it is unique in that the encoder actively controls the training process as is trained both learning processes two different roles. Experiments demonstrate that without regularization techniques, our generative model is robust to net architectures and easy to train, sample-wise reconstruction performs well in terms of sample features, and disentangled visual attributes are well modeled independent principal components.
critic to maximize the distance
INTRODUCTION
Minimax game provides an unsupervised learning method, which is widely used in generative mod- els such as generative adversarial nets (GAN) (Goodfellow et al., 2014; Chen et al., 2016; Radford closed-loop transcription framework (CTRL) (Dai et al.,
et al., 2015) and the recently-propen 2022). Generative modeling based minimax two-player game faces some problems, like the instability in training processes, the difficulty in maintaining the balance between the discrimina- and the generator (as in GAN) between the encoder and the decoder (as in CTRL), and the sensitiveness to net architectures (He et al., 2016a;b).
Maintaining balance and stability in the adversarial process attracts a lot of attention. The main- stream is provide constrained discriminator (Kurach et al., 2019). Some regularization tech- niques are provided, such as weight normalization (Salimans & Kingma, 2016), weight clip (Ar- jovsky et al., 2017), gradient penalty (Gulrajani et al., 2017), spectral normalization (Miyato et al., 2018), and adversarial lipschitz regularization (Terjék, 2019).
Different from the mainstream regularization methods, this paper considers the feasibility of letting the discriminator actively adapt to the rhythm of the generator. The reason why maintaining balance in the generative models via adversarial process is difficult that the generator and the discriminator tend to merely play against each other. However, balance will break sooner later once the dis- criminator learns faster than the generator. In contrast, generative models based on auto-Encoding like variational Auto-Enen auto-Encodiig (VAE) (Kingma & Welling, 2013; Lopez et al., 2018) tend to be stable, not facing instability and collapse problems. The reason is that the encoder and decoder in the Auto-Encoding framework learn and update themselves cooperatively quality and reduce data dimensions in the same direction. In one word, models work cooperatively rather than against each other. Inspired by this cooperation idea, this paper attempts to combine cooperative learning and adversarial learning in the generative model.
improve reconstruction
In this paper, a generative model via cooperative adversarial learning (CoA-CTRL) is proposed. CoA-CTRL employs the closed-loop transcription framework (CTRL) proposed by (Dai et al., 2022; Ma et al., 2022) and naturally combines the learning strategies of the adversarial process and coop- erative process. Firstly, like the discriminator in GAN, the encoder in CoA-CTRL. plays critic to

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maximize the feature distance between the real data and the transcribed data. Secondly, consistent with buto-Encoding, the encoder and decoder cooperatively minimize the difference between the real data and transcribed data. The confrontation and cooperation between the two take turns and intersect, which will actively keep the system in balance.
2 RELATED WORK
Outo-Encoding and its variants. Auto-Encoding is typical neural network for representation
learning and data dimension reduction (Kramer, 1991; Hinton & Zemel, 1993; Hinton & Salakhut- dinov, 2006). Auto-Encoding aims to learn the encoder Eg and the decoder D, simultancously, and this process can be demonstrated by equation (1). Generally, Auto Auto-Encoding tends learn from I.2 pixel-wise distance.
N 2 - Eo(2,(z))
min C(0, 17)
(1)
N
1=1
Generative adversarial nets (GAN). Generative adversarial nets (GAN) provides a generative model based on the adversarial process (Goodfellow et al., 2014; Chen et al., 2016). GAN includes discriminator and generator. The discriminator evaluates the performance of the generated images, and the generator tends to fool the discriminator. The two networks are trained based on the two- player minimax game by the value function V(G(m), D(0)) as equation (2) displays, where G(v) and (0) donate to the generator and discriminator respectively.
min max (nn. 0) = Ee-pp=)) D(x) + + -Essptt)loo([11 D(G(=))) 0
(2)
MCR² and CTRL Recently, Chan et (2022) and Yu et al. (2020) proposed new learning objective, the so-called principle of maximal coding rate reduction (MCR²), which is to learn the low-dimension intrinsic structures from high dimension data and obtain discriminative representa- tion between classes. Encoder / (x, 0) maps high dimension data X to the low dimension features Z. As shown in equation (3), MCR provides a method called coding rate (R( e)) to measure the compactness of learned feature Z integrally subject the distortion The rate reduction (AR) measures the distance in the feature space. In a special case of two classes as shown in equation (6), there will be data features Z and Ż. The distance between Z and can be measured by coding rate reduction (A 22,, 2)), that is, the difference between the coding rate of (ZU2) and the average sum of them (R₂).
R(Z,c) = log det (T (T+ +.7%"
1(x,0)
s(a.n)
{(,88)
Z
X
2
(4)
k(z(0.))= fogof
h(z, 0)) = ())
(5)
AR(Z,2) R(ZUŽ) _ (R(Z) R(2))
(6)
R.
min max T(0, m)) AR( 7
A (((Z(0),((011))
8
CTRL (Dai et al., 2022) provides flosed-loop framework based MCR², consisting encoder (f(x, 0)) and a decoder (g(2, 1)). As equation (7) shows, CTRL aims to transcribe data via mini- maxing coding rate reduction, in which h(z 8, 7) captures closed-loop map demonstrated by equations (4) and (5). The first segment (x: > z - ②) in (4) resembles Auto-Encoding, and the second segment +
2) resembles GAN. While GAN generates images from Random Gaussian Distribution noise, in CTRL, as (4) displays, decoder g(2, 11) maps from feature (which is encoded from X), and then encoder f(z, 0) maps X to feature 2. The distance between X
Where X Dxn refers data samples, ddx= refers features,
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and X is described by the coding rate reduction (AR) in equation (6). R(Z 2) refers to the coding rate of joint space of Z and Z. R. describes the average coding rate of Z and Ž. The encoder maximizes AR, and the decoder minimizes it. In this adversarial learning process, CTRL consistent with GAN (Goodfellow al., 2014).
Chan et al. (2022) shows that the gradient of A R will disappear when AR is large enough, which leads to low learning efficiency of encoder (ar, and decoder g(2, n)) The AR (the yellow ball) in the left part of Figure 1 demonstrates this situation. This is consistent with Arjovsky & Bottou (2017), which shows that when the distribution of real images and fake images does intersect in GAN, the gradient of the generator disappears. Therefore, keeping AR at small value is important. The R of the right part of Figure is better than the left part for the minimax game.
Figure 1: Two different representations compared based different rate reductions (Yu al., 2020; Chan al., 2022). R 2) is demonstrated by number of e-balls in the joint space of Z and 2 (all the balls). R. is the sum of a number of subspaces of Z (green ball) and 2 (red ball). A R describes their difference (yellow ball). While MCR prefers the left representation for large rate reduction, minimax game, the right is better.
COOPERATIVE ADVERSARIAL LEARNING
3.1 CLOSED-LOOP TRANSCRIPTION: ONE ENCODER, TWO ROLES
The way closed- loop transcription combines the structures of Auto Auto-Encoding and GAN is ingenious, as it gives the encoder two different roles. As the left part of Figure and equation (4) demonstrates. in the segment of x > f. the encoder takes the responsibility as the encoder in Auto-Encoding: while in the segment of + f + 2, the encoder takes the responsibility consistent with the discriminator in GAN. Different from the former works such VAE-GAN (Larsen al.. 2016) and BiGAN (Donahue et al., 2016: Dumoulin et al.. 2016) who add discriminator to estimate the decoder, closed- loop transcription trains only the encoder and the decoder, and it is the encoder that estimates the performance of the decoder.
yos
9e.
x
f(x.0)
f(x,0)
2
X
go:
g(z. m)
(zz, !))
(a) Forward propagation process
(b) Backward propagation process
Figure Forward propagation process (a) and backward propagation process (b) of CTRL. In the backward propagation process, encoder fax 0) was back-propagated three times, and therefore generated three gradient values ge 1912. and ges respectively.
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3.2
COOPERATIVE ADVERSARIAL LEARNING: TWO ROLES, LEARN TWICE
Detailed analysis of the encoder's two roles. CTRL's two-role setting and closed-loop framework contribute to the complexity of the objective distance function A Z, 2) as well as the training process. As shown in equations (4) and (5), in CTRL's closed-loop map H 8, 17). the encoder fxx,0) 0) is used twice. We can expand the closed-loop map as shown in equation (8), in which
2(0, m) is expanded to [(1,, 17. 82). We use 01 and B2 to refer to the encoder's first-time usage (X Z) and second-time usage (X + 2). We can also expand the distance function AR as shown in equation (9), in which AR Z, 2) is expanded to A AR( (8s). 2 (01 .7. 82)). 81. 82 and 0g all refer to the encoder faz, 0). but we mark it differently as it appears three times and its meanings in the distance function (Os), 2 17, 02)) are different. 0g and 0g represent the encoder when functions discriminator to transcribe data X and X, and 0, represents the encoder when it takes the responsibility of an encoder the segment of X + Z + 2.
H(X,0,n) (()((((,0)) 2(0,n) (00..
(8)
AR 2) - AR(Z(0s), 2(01.n,02) 02))
In the backward propagation process, as shown in the right part of Figure 2, encoder f(x, 0) will be calculated the gradient three times by the objective function (O()) 2(01,9,02), The three gradients are 9 90g and 90g, which are shown in equations (10), (11), and (12).
901 V A(
(10)
902
(11)
903 V 03
92)).9.62)
(12)
Cooperative adversarial learning based on encoder's two roles. Although CTRL gives the en- coder two roles, the original CTRL does not make use of the encoder's dual identity features in terms of its learning strategy (see Algorithm 2 in the Appendix). The original CTRL just follows the simple minimax game in which the encoder functions merely as a discriminator, and its original role of an encoder is ignored. In this paper, we provide cooperative adversarial learning as shown in Algorithm which makes use of the encoder's s two roles (encoder and discriminator).
Algorithm demonstrates cooperative adversarial learning. Cooperative adversarial learning com- prises two processes:
(1) Adversarial process.
We only use gradients gø2 and ges (when the encoder functions as a dis- criminator) in the adversarial process to maximize AR to provide an iteration learning signal. As shown in Algorithm 1, via the adversarial process, encoder @ updates itself by its role of discrimina- tor via Og (facing input X) and Og (facing input X) to enlarge A AR. This process is consistent with GAN and the original CTRL, and the equation is demonstrated by equation (13).
(2) Cooperative process. We use gei (when the encoder functions as the encoder in Auto-encoding), g92, gea, and 9n, together in the cooperative process to compress and transcribe data following the learning signal of AR. As shown in Algorithm 1, via the cooperative process, we optimize 61, 0g. 0g. and 17, which are all elements in the closed- transcription, to compress data and transcribe data via the learning signal of AR. Equation (14) demonstrates this process.
max AR(F(X, 0), H(X,, m)) = A.(
(13)
I adversarial
min
A)( H(, 0, m)) = (00)))
(14)
cooperation
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Algorithm 1 Cooperative Adversarial Learning
Require: a, learning rate. ratio, CoA-ratio. c², coding rate parameter. bs, batch size.
Require: 0, init parameters of the encoder. v), init parameters of the decoder.
while has not converged do
Adversarial process to provide iteration learning signal AR
Sample X + 20) bis
i-1 batch from the real data
Z = f(z,0), X = (Z, 2 f(X,0)
go2 Vm2 VR (0))), 2(0,,
903 < V03 (( 2(01.9)02)
00 < 902 983
+ 0 + ratio x a x Adam(ga)
Cooperative process to compress and transcribe via learning signal AR Z = (x, (Z. 2 f(X,0)
901 (Z))
902 < VoIAR(Z(03)) 2(0,
983 (e))
99 < + 962 + 983
9n < (((3))(((s). 2(01n7,02)
8 - 0 - x Adam(
7) - 7 - a X Adam
end while
Auto-Encoding optimizes the reconstruction quality based on pixel-wise loss, however, CoA-CTRL. optimizes the reconstruction quality based the distribution distance (AR) in the feature space, and the feature distribution is defined by the encoder itself, which can be seen as a self-consistent learning strategy in the closed-loop framework. When the encoder tries find the distribution distance and maximizes it in equation (13), it provides an iterative learning signal (AR) for the reconstruction task in equation (14). The encoder takes the responsibility for two roles and learns twice. It minimizes what it maximizes, which is self-consistent learning strategy (Ma et al., 2022).
In this sense, CoA-CTRL. is adaptive learning strategy and naturally unifies an adversarial pro- cess consistent with GAN and a cooperative process consistent with EicooiEn, which will contribute to its learning quality and stability. In conclusion, cooperative adversarial learning that follows the value function T(B, n7) is displayed in equation (15):
min max T(B.n) P.9
(((
(15)
3.3 COOPERATIVE ADVERSARIAL RATIO (COA-RATIO): HYPERPARAMETER TO ADJUST THE COOPER ATIVER PROCESS AND THE ADVERSARIAL PROCESS
Active control of the adversarial process and the cooperative process. As discussed before, the encoder works and learns in two different roles in the adversarial process and the cooperative process the encoder in both processes, we can actively
respectively. Therefore, through the functioning control the adversarial process and the cooperative process. We introduce a hyperparameter cooper- ative adversarial ratio (CoA-ratio), which is the ratio of the encoder's ' learning rate in the adversarial process to its learning rate in the cooperative process, to adjust the learning rate of the encoder in two learning processes. In every iteration, before going through equation (13), the learning rate of the encoder would multiplied by the CoA-ratio, and before going through equation (14), encoder's s learning rate would be restored to the previous value.
Balance achieved through active control. The discriminator and generator in GAN work in the opposite direction. The learning process of the discriminator is hard to control, and therefore the balance of the training is hard to keep. Benefited by the encoder learning twice in the cooperative and adversarial processes and its different learning rates and speeds realized by the CoA-ratio, the encoder becomes the controller and regulator of the training process. The training process and
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the loss value AR thus can be easily controlled without paying too much attention to constrained network design.
3.4 ADVANTAGES AND DIFFERENCES
Training stability. Encoder's learning twice provides a way to actively balance the learning pro- cesses through its two roles. As equation (15) shows, the encoder not only maximizes AR but also minimizes it, which relieves the need to design special networks or adjust parameters. Com- pared with former balance techniques. CoA-CTRL does not add constraint techniques computing processes. It is simple and computationally efficient. In experiments, classic deep nets ResNet18 8, ResNet50, and ResNet101 (He et al., 201 6a:b) are used to validate CoA-CTRL's active balance.
campse-consistent reconstruction. As mentioned in 3.2, CoA-CTRL naturally unifies the learning strategies of Auto-Encoding and GAN, which will help the encoder learn better and faster. Other than that, it will benefit the sample-wise reconstruction. Sample-wise consistent reconstruction (((())) RS x is the ideal solution to & (0), Z(0, 1))) R O, and AR(Z(0), Z(0, n)) is determined by encoder [xx 0) and decoder g(2, n). CTRL (Dai et al., 2022) would minimize Á &R(O, 11 merely through the decoder g(2. 77). which would give an approximate optimization choice that results in poor sample- comple-wise consistency. However, if we optimize AR(O, n)) by decoder g(2, 1) and encoder (ar 0). the optimization process would become simple, and the ideal comple-consisten solution would be easily obtained. In addition, cooperative adversarial learning via closed- transcription produces good disentangled feature space. Later experiments will demonstrate this advantage.
Simpler. As Auto-Encodiig, its variants (Kingma & Welling, 2013), and GAN all gain lot of atten- tion in the generative model area, many works have attempted to combine Auto-Encoding and GAN, like Bigan (Donahue et al., 2016), ALI (Dumoulin al., 2016). adversarial autoencoders (Makhzani et al., 2015) and VAE-GAN (Larsen et al., 2016). Different from those attempts, CoA-CTRI. imple- ments the closed-loop transcription (Dai et al., 2022), introduces the cooperative process, and invites no other discriminator. The encoder learns twice in different roles within one iteration without in- vesting more computing resources. Stability and balance are controlled actively without regulation techniques, which contributes to computing resource saving compared to other regulation techniques like spectral normalization (Miyato et al., 2018). Also, the original CTRL always depends on big batch sizes, while cooperative adversarial learning could reduce this demand.
EXPERIMENTS
4.1 SETTING
In this paper, we intend to justify two main advantages of CoA-CTRL: firstly, CoA-CTRL's bustness to different net architectures through our cooperative adversarial learning; secondly, CoA- CTRL's sample-consistent reconstruction. To conduct the experiments, we use two types of en- coders: deep encoders and normal encoders, and both types of encoders will be paired with one type of decoder. We conduct the experiments with deep encoders on the diverse data set CIFAR-10 10 (Krizhevsky et al., 2009) and STL-10 (Coates et al., 2011), as well as the facial data set Celeb-A (Liu et al., 2015), aiming to demonstrate CoA-CTRL's active balance. We conduct the experiments with normal encoders on MNIST (LeCun et al., 1998), CIFAR-10, and ImageNet- 1 (Russakovsky et al., 2015), aiming to prove CoA-CTRL's sample-wise consistency. More details of the experiment setting could be found in Appendix A.2.
4.2 EMPIRICAL VERIFICATION OF COA-CTRL's ACTIVE BALANCE
4.2.1 ACTIVE BALANCE TO NET ARCHITECTURES
To verify CoA-CTRLS active balance, we conduct several comparative tests on CIFAR-10, using ResNet18, ResNet50, and ResNet101 as the encoder, and the widely used 8-layer resnet (De8) (Miyato et al.. 2018) as the decoder. We intend to prove that even with an unbalanced combination of the encoder and decoder, CoA-CTRI. can perform well in stable manner. We apply the same settings on GAN and CTRL, aiming to compare their stability and performances with COA-CTRL. Results in Table show that CoA-CTRL works well, while GAN and CTRL fail and collapse in the training process.
To quantity CoA-CTRL's performance, we test CoA-CTRL by the widely used Inception score (IS) (Salimans et al., 2016) and Fréchet Inception Distance (FID) (Heusel et al., 2017). We further com- pare CoA-CTRL's IS and FID with other major generative models, which are displayed in Table 2.

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Interested in the parts feature dimension (nz) and batchsize (bs) play in CoA-CTRL's performance and stability, we additionally adjust nz and batchsize to see whether CoA-CTRL can maintain bal- ance. As shown in Table 1, we find that CoA-CTRL works well with different combinations of nz and batchsize, and it performs better when we increase nz and batchsize at the same time.
We also explore the loss value AR in the training process. As shown in Figure 3, CoA-CTRL keeps low and stable A AR and high R( R(ZUZ). while CTRL shows unstable training loss AR. CoA-CTRL's stable training loss contributes to its training success and excellent performance.
Table 1: Stability and performance of CoA-CTRL compared with GAN and CTRL on CIFAR- 10. Experiments show that CoA-CTRL gets excellent performances in a stable manner even with unbalanced settings of deep encoder and shallow decoder. Avg. of R and Avg. of A R refer to the average value of R(Z U 2) and AR in the training process. 1 means higher is better, and means lower better.
Methods
Encoder/
Decoder/ Generator
nz
bs
Result
Avg. of R
Avg. of AR
IS 1
FID 4
Discriminator
Comparisons of stability and performance
GAN
ResNet18
De8
128
128
fail
GAN
ResNet18
De8
128
512
fail
CTRL
ResNet18
De8
128
512
fail
45.57
9.77
CoA-CTRL
ResNet18
De8
512
succeed
65.03
1.65
7.94
10.49
CoA-CTRL
ResNe150
De8
128
512
succeed
64.04
L.84
7.82
11.17
CoA-CTRL
ResNet101
De8
128
512
succeed
63.79
1.76
7.12
19.12
(2) Ablation study on nz and batchsize
CoA-CTRL R ResNet18
De8
128
512
succeed
65.03
1.65
7.94
10.49
CoA-CTRL
De8
128
1024
succeed
63.85
1.25
7.91
10.92
CoA-CTRL
ResNet18
De8
512
succeed
127.92
6.15
7.38
12.72
CoA-CTRL
ResNet18
De8
256
1024
succeed
127.15
2.81
8.21
9.54
CoA-CTRL
ResNet18
De8
512
1024
succeed
244.38
11.17
7.60
11.61
14-
60-
L,05
LO55
20-
10
.6033
2000
66633
4200
60083
$2500
Number
iterations
(a) CTRL
(b) CoA-CTRL
Figure 3: Loss evaluation of CTRL and CoA-CTRL in the training process CIFAR-10, using ResNet18. CoA-CTRL keeps AR in stable curve even with an unbalanced setting of a deep encoder and shallow decoder.
4.2.2 ASSOCIATION BETWEEN THE LOSS VALUE AR AND PERFORMANCE
As shown in Table and Figure 3, CoA-C CoA-CTRL's performance seems to be associated with AR. As discussed in section 3.3, introduce CoA-ratio to adjust the learning rate of the encoder in the ad- versarial process and the cooperative process, which would influence the loss value R.. Therefore, set CoA-catio at 1.51.5 and 2.0 respectively explore the influence of AR CoA-

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Table 2: Comparison of performances of CoA-CTRI. and other generative methods CIFAR-10
and STL-10. Data of other generative models are cited from relevant papers. For CoA-CTRL (1), the nz is 128, and the bs is 512; for CoA-CTRL (2), the nz 256, and the bs is 1024.
CIFAR-10
STL--10
Methods
ISt
FID
ISt
FID4
SNGAN
8.2
21.7
9.1
40.1
WGAN-GP
7.9
29.3
WGAN-ALP
8.3
13.0
SSGAN
17.1
LOGAN
8.7
17.7
BIGGAN
9.2
14.7
DCVAE
8.2
17.9
8.1
41.9
CTRL
8.1
19.6
8.4
38.6
CoA-CTRL (1)
7.94
10.49
8.68
22.96
CoA-CTRL (2)
8.21
9.54
9.28
18.54
CTRL's performance. We conduct the experiment on CIFAR-10, using ResNet18 as the encoder, with nz and batchsize set at 128 and 512. As shown in Table 12 and Figure 6 in the Appendix, associated with different values of AR and R. and the performance score
different CoA-ratios IS and FID are highly influenced by A R. but the good thing is that the change A A stable. This paper just points that CoA-ratio, AR, and performance are highly associated. Future works will explore the mechanisms.
4.2.3 EXCELLENT PERFORMANCE ACHIEVED THROUGH SIMPLE DECODER
performance with other generative models on CIFAR-10 and STL-10. The data in Table 2 is directly cited from those relevant papers except for CoA-CTRL. For CoA- CTRL, We use ResNet as the encoder on both CIFAR-I0 and STL-10. Table 2 shows that CoA- better with simple decoder and experiment setting, compared with other generative models, such as GANs with regularization techniques (SNGAN, WGAN-GI WGAN-ALP) (Miyato et al., 2018; Gulrajani et al., 2017: Terjék, 2019), self-supervised GAN (Chen et al., 2019), latent optimisation GAN (LOGAN) (Wu et al., 2019), complex model GAN (BIGGAN) (Brock et al., 2018), recent combination of GAN and VAE (DCVAE) (Parmar al., 2021), and the original CTRL (Dai et al., 2022). Compared with CTRL, the FID value of CoA-CTRL is decreased by 10.06 on CIFAR-10 and 20.06 on STL-10. The improvements are clear and substantial.
We compare
4.3 SAMPLE-WISE CONSISTENCY
CoA-CTRL performs well on sample-wise reconstruction terms of sample features, which is demonstrated by our experiments on several mainstream data sets using normal encoders. Figure shows CoA-CTRL's reconstruction performance on MNIST compared with CTRL. We can see that CoA-CTRL's reconstruction almost the
as the original input, better and more consistent than CTRL. For CIFAR- 10 and ImageNet-11 1k (Russakovsky et al., 2015), we use networks listed through Table Table in the Appendix. We run 20,000 iterations on both data sets. Figure 4 displays CoA-CTRI's performance on CIFAR-10 and ImageNet- kk. We can see that CoA-CTRI. recon- structs well in terms of features, color, and classes, which is benefited from cooperative adversarial learning and a loss function based the feature space.
4.4 DISENTANGLED FEATURE SPACE
The latent space GAN has no certain meanings and lacks inverse maps from data to the latent space. Some following works discussed this issue (Chen et al., 2016; Karras et al., 2019; 2020; Tov et al., 2021). The latent space in CoA-CTRL has clear and disentangled meanings. CoA- CTRL possesses the concept of dual consistent maps, + and in the Appendix are the generated samples of CIFAR-10 10 along independent principal components. We select the top 10 components with every referring to component from top to bottom. We can see that different shapes, styles, backgrounds, and other visual attributes are well modeled in different principal components, and the images vary with the scale value. In addition, we test
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(c) by CTRL
(d) CIFAR-IO X
(e) Å by CoA-CTRL
X by CTRL
(g) ImageNet- Ik
(h) ImageNet-Ik by CoA-CTRL
Figure 4: Comparison of sample-wise reconstruction. CoA-CTRL performs well in sample-wise reconstruction on MNIST, CIFAR-I0, and ImageNet-Ik.
the classification accuracy on MNIST based on feature representations with one Nearest Neighbor (1NN) classifier. As Table 13 in the Appendix shows, CoA-CTRL's performance is competitive compared with other methods (Springenberg, 2015; Kingma Welling, 2013; Donahue al., 2016; Dumoulin al., 2016; Makhzani et al., 2015; Parmar et al., 2021; Dai et al., 2022).
DISCUSSION AND CONCLUSION
In this paper, we propose cooperative adversarial learning, and based on this new learning method and closed-loop transcription, we build a promising generative model, which possesses the prop- erties of active balance, better generative performance, and disentangled latent space. Other than that, we find it competitive in unsupervised representation. Although cooperative adversarial learn- ing provides way balance deep nets, some questions still unclear. For example, whether deeper encoder would benefit to better performance, and how big a coop-ratio or AR is best for the training process and model performance. These questions deserve further explorations.

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Under review as a conference paper at ICLR 2023
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Tim Salimans, Ian Goodfellow, Wojeiech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016.
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A APPENDIX
A.1 LEARNING STRATEGY OF THE ORIGINAL CTRL
Algorithm 2 Original CTRL's learning strategy
learning rate. ratio, CoA-ratio. e², coding rate parameter. bs, batch size, Require: 0, init parameters of encoder. 17, init parameters decoder. while 77 has not converged do
Require:
Sample X + (8)6 69
i=1 a batch from the real data
Z = fax 0), X = (Z, m), 2 = f(X,0)
Do2 < Vn ( (03), (1,,, 02))
D03 <- Vos Z (0s) 2(0,,
90 < 902 + 9e3
0 4 0 ratio
Ad)
Z [(r 0), 8 Z, 1, 2 s(X,0)
9n - V,AR(Z ( 02))
17 f 1) a x (g)
end while
A.2 EXPERIMENT SETTING
A.2.1 EXPERIMENTS USING NORMAL ENCODERS
We conduct the experiments using nets in DCGAN (Radford et al., 2015) and some other simple nets on MNIST, CIFAR-10, and ImageNet- 1k, aiming to justify CoA-CTRL's sample-wist consistency. The details of the networks can be found through Table 3 to Table 7. The experiments are fair comparisons to the original CTRL, and the only difference is the learning strategy. For experiments with normal encoders, the encoder and the decoder have similar volumes. We set the hyperparameters 31 and 32 of the optimizer Adam (Kingma & Ba, 2014) at 0.0 and 0.9 for MNIST, and at 0.5 and 0.9 for CIFAR-10 and ImageNet-Ik. lk. We set the learning rate 0.0001 and apply linear decay. 2² is set at 0.5. We adjust the CoA-ratio at 1.3 for MNIST and 1.5 for other data sets. For MNIST, we set batchsize at 256 and run 10,000 iterations. For CIFAR-10, 10, we set the batchsize at 512 and run 20,000 iterations. For ImageNet-1k, we set the batchsize at 128 and run 20,000 iterations.
conduct
A.2.2 EXPERIMENTS USING DEEP ENCODERS
We conduct experiments using deep encoders on CIFAR-10, STL-10, and Celeb-A. The settings of the experiments using deep encoders are as follows. Adam (Kingma & Ba, 2014) would be used as the optimizer. The learning rate set at 0.0001, and the linear decay is applied. For the classic hyperparameters 81 and $g. we set them at 0.0 and 0.9 respectively. We fix e² at 0.5 in all experiments. CoA-ratio is set at 1.5. The value of nz is set at 128. The batchsize is 512. For the decoder. adopt the widely used networks in DCGAN (Radford al., 2015). SNGAN (Miyato et al., 2018), and CTRL (Dai et al., 2022). The details can be found in Table 5, Table 9, and Table 10. For the encoder, resnet (ResNet50), and 101 fll-layer preaction resnet (ResNet101) to verify CoA-CTRL's stability and robustness to deep nets.
apply deep nets 18-layer preaction resnet (ResNet18). 50-layer preaction
As for ResNet18, ResNet50, and ResNet101 in this paper, we use preaction (He et al., 2016b) and average pooling to downsample, which will contribute to better feature extraction. For STL-10 and Celeb-A, we add a downsample at the first ResBlock of ResNet18. Spectral normalization (Miyato al., 2018), batch normalization (Ioffe & Szegedy, 2015), or other regulation techniques are not applied, instead, just simple and standard convolution layer without constraint is employed.
We run 10,000 iterations MNIST, 100,000 iterations CIFAR-10, 150,000 iterations on STL-10 and Celeb-A. We resize the resolution of MNIST to 32 x 32, STL-10 to 48 x 48, and Celeb-A to 64 x 64.
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Table 3: Decoder for MNIST
Table Encoder for MNIST
2 E Rd
x: E R 32X32XX
4x4.stride=l.pad-00 deconv.BN256 ReLU
4, stride=2, pad=1 conv 64 IReLU
4x4.stride=2,padd= Decconv.BNT2. ReLU
4 x 4, stride=2, pad=1 conv. BN 128 IReLU
4x4.strido=22pad==1 deconv.BNo ReLU
4 x 4, stride=2, pad=1 conv. BN 256 IReLU
4x4.stridc=2.pad=1 deconv decon Tanh
x 4, stride= pad=0 conv 128
Table 5: Decoder for CIFAR-10
Table 6: Encoder for CIFAR-10
2 E Rudss
R3233221
dense 4 4x 256
ResBlock down 64
ResBlock up 256
ResBlock down 128
ResBlock up 256
ResBlock down 256
ResBlock up 256
4 x 4, stride=1, pad=)) conv 512
BN, ReI.U, 3x3 conv, 3 Tanh
Table 7: Decoder for ImageNet
Table 8: Encoder for ImageNet
z E Rdim
x' E R
4x4.stride=1.padd00 (econv.BN512 ReLU
4 x 4, stride=2, pad=1 conv 64 IReLU
4x4.stride=2,pad== Reconv.BN256 ReLU
4 X 4, stride=2, pad=1 conv. BN 128 IReLU
4x4sstrdd==2ppad=11 deconv.BN128 ReLU
x 4, stride=2, pad=1 conv. BN 256 IReLU
4x4.stridc=2.paa- dew.ne. ReLU d
4 x 4, stride=2, pad=1 conv 512 IReL.U
4x4.stride=2.pad=-1 deconv 3 Tanh
4 x 4, stride=1, pad=0 conv 1024
Table 9: Decoder for Celeb-A
I: E ReIxbix3
ResBlock down 64
ResBlock down 128
ResBlock down 256
ResBlock down 512
4 x 4, stride=1, pad=0 cony 1024
Table 10: Decoder for STL-10
z E R dsm
dense + 6 x 6x 512
ResBlock up 256
ResBlock up 128
ResBlock up 64
BN, ReLU, 3x3 cony, 3 Tanh
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A.2.3 STRUCTURES OF RESBLOCK
BN
15013
Cony
Upsample
KeLU
Conv
AusiPool
(a) Resblock down
(b) Resblock up
Figure 5: Structure of resblock down and reblock up.
A.3 COA-CTRL's ROBUSTNESS BATCH-SIZE
Table 11: Comparison between CoA-CTRA and the original CTRL in terms of robustness to batch size, using normal encoder and decoder as displayed in Table and Table 6. Fail means the model collapses.
Batch size
64
128
256
512
1024
CTRL CoA-CTRL
Fail succeed
Fail succeed
Fail succeed
succeed succeed
succeed succeed
A.4 ABLATION STUDY ON COA-RATIO
Table 12:
Ablation study on CoA-ratio on CIFAR-10. Avg. of and Avg. of AR refer to the average value R U Z) and AR in the training process.
CoA-ratio
1.25
1.5
1.75
2.0
Avg. of R
64.08
65.03
63.45
64.16
Avg. AR
0.62
1.65
3.92
4.07
ISt
6.55
7.94
7.02
6,64
FID4
22.34
10.49
38.04
38.20
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50
53
MMF 25
LO35
(uzz) n2oo
9221
10
30
Number of ittretions
Number of iterations
(a) CoA-ratio:1.22
(b) CoA-ratio:1.5
27
12
Loss
12
ju42 4221
16
20000
20000
00639
100905
20083
13900
62083
82000
100000
Number of iteretions
Number of iterations
(c) CoA-ratio:1.2
(d) CoA-ratio:2.0
Figure 6: Loss evaluation of CoA-CTRL in the training process on CIFAR-IO, using ResNet18 as the encoder and De8 as the decoder. Different CoA-ratio of 1.25, 1.5, 1.75, and 2.0 are used.
A.5 COA-CTRL'S SAMPLE-WIST RECONSTRUCTION ON STL-10 AND CELEB-A
(a) STL-10 X
(b) STL- X by CoA-CTRL
Celeb-A X
(d) Celeb-A X by CoA-CTRL
Figure 7: Comparison of sample-wise reconstruction. CoA-CTRL performs good sample-wise construction STL-10 and Celeb-A.
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A.6 DISENTANGLED FEATURE SPACE AND CLASS-WISE ACCURACY
airplane
(b) automobile
(c) bird
(d) cat
dog
(g) frog
(h) horse
ii ship
truck
Figure 8: Generated samples of ten classes along 10 principal components from the feature subspace on CIFAR- 10. Every row refers to principal component, and the scale value varies from -1.5 to 1.5.
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Table 13: Class-wise accuracy performance with respect unsupervised representation MNIST
Method
VAE
BIGAN
catGAN
AAE
DC-VAE
CTRL
CoA-CTRL
Accuracy
97.12%
97.39%
95.7%
95.9%
98.71%
89.12%
95.89%
18


Reviews:
[{"review_id": "ugMtu04rjMZ", "reviewer": "Reviewer_AoR2", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposed a novel generative model (CoA-CTRL) that integrates cooperative adversarial\nlearning and closed-loop transcription. Experiments on MNIST, CIFAR-10, STL-10, and Celeb-A demonstrate the robustness and consistency of the proposed model.", "summary_of_the_review": "This paper has some interesting points, but this version lacks a deeper explanation of the proposed framework. Some theoretical or experimental explanations are encouraged to improve the paper. Also, it lacks comparison to some existing works, this paper needs to compare their performance on a better-implied baseline. Thus I give 5 at the current time. \n", "strength_and_weaknesses": "Pros\n\n- This paper proposed a novel generative model with cooperative adversarial\nlearning and closed-loop transcription. The idea of integrating cooperative adversarial\nlearning and CTRL to improve the robustness of generative models is interesting. \n- The experiments evaluate the robustness of the model on some datasets.\n\nCons\n\n- Previous works [1,2] have already improved the robustness of the generative models, can you explain the advantage of the CoA-CTRL with these previous works?\n- The datasets used in this paper are somewhat small in comparison. Just wondering about the performance of CoA-CTRL on CIFAR-100 and ImageNet\n\n[1] Regularizing Generative Adversarial Networks under Limited Data \n\n[2] Training generative adversarial networks with limited data", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "osY-pn_CS2", "reviewer": "Reviewer_rrv8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper designs a generative model for effective cooperative adversarial learning through closed-loop transcription.  In the training, both the encoder and decoder are trained simultaneously for cooperative adversarial learning. The experimental results show effectiveness of the proposed strategy.", "summary_of_the_review": "The techinical novelty is below the bar of ICLR.", "strength_and_weaknesses": "Pros:\n+ The idea of cooperative adversarial learning is an important and interesting direction. \n\nCons:\n- The paper does not clearly explain the motivations and the ideas. \n- The details of the proposed framework are not illustrated in a clear way.\n- The experimental results do not fully support the conclusion of the paper.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "bCo3wN5CfO", "reviewer": "Reviewer_qmAD", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper tries to combine the autoencoding (cooperative learning) and GAN (adversarial learning) in a unified framework such that it proposes to apply the encoder to play two different roles and control different objectives.", "summary_of_the_review": "see above.", "strength_and_weaknesses": "This paper has already got published in Entropy 2022 [1]. Why is it submitted here?\n\n[1] https://www.mdpi.com/1099-4300/24/4/456", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "5xnWnCyvfup", "reviewer": "Reviewer_1daZ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposed to extend the Closed-Loop Transcription (CTRL) (Dai et al., 2022). While the original CTRL formulates the task of generative modeling as a minimax game between the encoder and the decoder, the proposed method simply employs an additional cooperate term that minimizes the rate reduction in both of the players. The proposed method is evaluated with several GAN and VAE-GAN baselines including CTRL and demonstrated some improvements on simple datasets like CIFAR10 and MNIST.", "summary_of_the_review": "I lean toward rejecting this paper due to non-trivial flaws in the method and experiments. ", "strength_and_weaknesses": "Strength:\n- The idea presented in the paper is clear.\n- The paper is easy-to-read in general and reasonably self-contained.\n\n\nWeakness:\n- Based on my understanding, the adversarial and cooperate optimization in Eq.(9) and Eq.(10), respectively, give exactly the same gradient with respect to the encoder parameters but in the opposite direction (i.e., only the sign of the gradient is different) since both optimizations are with respect to the same decoder parameters \\gamma. Considering that the authors proposed to apply different learning rates for the encoder when optimizing Eq.(9) and Eq.(10) (i.e., named CoA-ratio), these two optimizations eventually collapse to the same adversarial optimization in Dai et al., 2022 but applying different learning rates for encoder and decoder. Please clarify it in the rebuttal.\n\n- The experiments are generally not convincing. As discussed in Section 4.2.1, the authors intentionally employed unbalanced encoder and decoder to make training unstable. As a result, all baselines such as GAN and CTRL fail to learn even an extremely simple dataset like CIFAR10. This is an unreasonable result considering the advances in the field and the low complexity of the dataset. The authors instead should present the main comparison results with the balanced setting and use Table 1 as an additional analysis. \n\n- Figure 5 is supposed to be evidence to show that the proposed method learns disentangled representation, yet the results display the opposite. As it shows, changing one factor of variation (each row in Figure 5) leads to changes in multiple attributes, which show evidence of entangled representation.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "3DLkumIy2Fg", "reviewer": "Reviewer_kFbE", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes an optimizatin technique (CoA-CTRL) to stablize the training of closed-loop transcription framework (CTRL). CTRL doing a minimax game of an encoder model $f(\\theta)$ and a decoder model $g(\\eta)$ over the coding rate reduction in the data feature space $Z$. The encoder in CTRL has two roles, the encoder role in Auto-Encoding and the discriminator role in GAN, while it is only optimized with the adversarial process.  CoA-CTRL thus proposed to optimize the encoder for both roles. This equals to first maximize $\\theta$ over the coding rate reduction and then minimize $\\theta, \\eta$ over the same loss. The experiments suggest that this optmization design gives more stable training than CTRL and achieve better performance.", "summary_of_the_review": "I think this paper proposes a technique that makes a more stable training under the CTRL framework. But some questions and concerns are there to be solved. Please check the Strength And Weaknesses for more details.", "strength_and_weaknesses": "The good part of this paper lies in that the training process it proposed seems to be more stable than CTRL and achieves better performance.\n\nSeveral concerns and questions I have are:\n1. Limited novelty: Most of the designs (like the minimax game over coding reduction rate, the different roles of encoder, etc.) and equations of this paper are from CTRL, while this paper only slightly change the optimization design, i.e. optimizing both $\\theta$ and $\\phi$ in the minimizing part. So for me, instead of calling it a new generative model, I will only see it as an improvement that stablizes the training of CTRL.\n2. Sensitive to hyperparameter: From Table 2, we can see that the performance of CoA-CTRL is highly sensitive to the CoA-ratio. Thus, although there may exist a better solution of CoA-CTRL than CTRL, carefully parameter tuning may be needed to reach this result, which may limited the usage of this technique.\n3. How can new samples be generated after the model is trained? From figure 2 , it seems that the $z$ and $\\hat{z}$ used in training are all inferred from observations $x$ and $\\hat{x}$ instead of being directly sampled from a simple Gaussain distribution like GAN and no constraint for the prior distribution is applied to $z$ as the VAE. Then how can $z$ or $x$ be sampled after the model is trained? Also the paper only shows the reconstruction results. How are the generated samples look like?\n4. The class-wise accuracy of CoA-CTRL in table 4 seems not be high enough. In fact, besides CTRL and catGAN , CoA-CTRL can not beat any other baselines. Consider the MNIST task is very simple, I would regard the performance differences as obvious and can not agree that CoA-CTRL achieves comparable results as its baseline models. \n5. Besides on the simplest MNIST dataset, the sample reconstruction results shown in figure 4 and 6 are not good enough. Many figures are blurry and details are lost.\n6. This paper proposes to cooperatively learn encoder and decoder, then how is this different/related to [1]. Also, as a model trained under new generative framework, besides GAN and VAE, other generative frameworks (like score-based model, diffusion model, energy-based model, etc.) should also be considered as baselines. \n\n[1] Cooperative learning of descriptor and generator networks. PAMI, 2020", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject