Name: LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
Uploaded: 2024-04-16
Duration: 11 min 57 s
Description: Hi, I am presenting this article named LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS. This paper was an oral presentation at ICLR 2019. The team consisted of three researchers: Brock from Heriot-Watt University, Donahue and Simonyan from Google DeepMind..

[Audio] Hi, I am presenting this article named LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS. This paper was an oral presentation at ICLR 2019. The team consisted of three researchers: Brock from Heriot-Watt University, Donahue and Simonyan from Google DeepMind.. 

Scene 1

LARGE SCALE GAN TRAINING FOR
HIGH FIDELITY NATURAL IMAGE SYNTHESIS

Full citation:  Brock, A., Donahue, J. and Simonyan, K., 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis [Online]. arXiv.org [Online]. Available from: https://doi.org/10.48550/arxiv.1809.11096.

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[Audio] Recent advancements in image generation struggle to produce high-resolution, diverse samples from datasets like ImageNet. Generative Adversarial Networks (GANs) offer promise but face challenges in stability and performance. Researchers propose a solution: applying orthogonal regularization to the generator, allowing control over sample fidelity and diversity via latent space truncation. This modification achieves state-of-the-art results in class-conditioned image synthesis on ImageNet, addressing instability challenges inherent in large-scale GAN training.. 

Scene 2

Zhang, H., Goodfellow, I., Metaxas, D. and Odena, A., 2019. Self-Attention Generative Adversarial Networks [Online]. arXiv.org [Online]. Available from: https://doi.org/10.48550/arxiv.1805.08318.

Brock, A., Lim, T., Ritchie, J.M. and Weston, N., 2017. Neural Photo Editing with Introspective Adversarial Networks [Online]. arXiv.org [Online]. Available from: https://doi.org/10.48550/arxiv.1609.07093.

= 0.7 = 0.5 v, —o —0.5 —
ligure 8. The effect of truncation trick as a function of styk scak
V. When we —+ 0, all faces converge to the "mean" face
of FFHQ. This face is similar for all trained and the in-
terpolation towards it never seems to cause artifacts. By applying
regative scaling to styks, we get the corresponding opposite or
' 'anti-face". It is interesting that various high-level attributes of-
ten flip between tlr opposites, including viewpoint, glasses, age,
coloring, hair length, and often

[Audio] The study finds that stability in training doesn't stem solely from the generator or discriminator but emerges from their interaction. Symptoms of pathological conditions can be tracked, showing the need for reasonable training conditions. Tight constraints on the discriminator can ensure stability but harm sample quality. Loosening this condition post satisfactory training can improve data generation effectiveness.. 

Scene 3

G.I INTERVENING BEFORE COLLAPSE
The symptoms of collapse are sharp and sudden, with sample quality dropping from its peak to
its lowest value over the course of a few hundred iterations. We can detect this collapse when the
singular values in G explode, but while the (unnormalized) singular values grow throughout training,
there is no consistent threshold at which collapse occurs. This raises the question of whether it is
possible to prevent or delay collapse by taking a model checkpoint several thousand iterations before
collapse, and continuing training with some hyperparameters modified (e.g., the learning rate).
We conducted a range of intervention experiments wherein we took checkpoints of a collapsed
model ten or twenty thousand iterations before collapse, changed some aspect of the training setup,
then observed whether collapse occurred, when it occurred relative to the original collapse, and the
final performance attained at collapse.
We found that increasing the learning rates (relative to their initial values) in either G or D, or both G
and D, led to immediate collapse. This occurred even when doubling the learning rates from 2 • 10¯4
in D and 5 • in G, to 4 • in D and 1 • 10-4 in G, a setting which is not normally unstable
when used as the initial learning rates. We also tried changing the momentum terms (Adam's '31
and (32), or resetting the momentum vectors to zero, but this tended to either make no difference or,
when increasing the momentum, cause immediate collapse.
We found that decreasing the learning rate in G, but keeping tlw learning rate in D unchanged could
delay collapse (in some cases by over one hundred thousand iterations), but also crippled training—
once the learning rate in G was decayed, performance either stayed constant or slowly decayed.
Conversely. reducing the learning rate in D while keeping G's learning rate led to immediate collapse.
We hypothesize that this is because of the need for D to remain optimal throughout training—if its
learning rate is reduced, it can no longer "keep up" with G, and training collapses. With this in mind,
we also tried increasing the number of D steps per G step, but this either had no effect, or delayed
collapse at the cost of crippling training (similar to decaying G's learning rate).
To further illuminate these dynamics, we construct two additional intervention experiments, one
where we freeze G before collapse (by ceasing all parameter updates) and observe whether D remains
stable, and the reverse, where we freeze D before collapse and observe whether G remains stable.
We find that when G is frozen, D remains stable, and slowly reduces both components of its loss
towards zero. However, when D is frozen, G immediately and dramatically collapses, maxing out
D's loss to values upwards of 300, compared to the normal range of O to 3.
This leads to two conclusions: first, as has been noted in previous works (Miyato et al., 2018;
Gulrajani et al., 2017; Zhang et al.. 2018), D must remain optimal with respect to G both for stability
and to provide useful gradient information. The consequence of G being allowed to win the game is a
complete breakdown of the training process, regardless of G's conditioning or optimization settings.
Second, favoring D over G (either by training it with a larger learning rate, or for more steps) is
insufficient to ensure stability even if D is well-conditioned. This suggests either that in practice, an
optimal D is necessary but insufficient for training stability, or that some aspect of the system results
in D not being trained towards optimality. With the latter possibility in mind, we take a closer look
at the noise in D's spectra in the following section.

[image] This leads to two conclusions: first, as has been noted in previous works (Miyato et al., 2018;
Gulrajani et al., 2017; Zhang et al., 2018), D must remain optimal with respect to G both for stability
and to provide useful gradient information. The consequence of G being allowed to win the game is a
complete breakdown of the training process, regardless of G's conditioning or optimization settings.
Second, favoring D over G (either by training it with a larger learning rate, or for more steps) is
insufficient to ensure stability even if D is well-conditioned. This suggests either that in practice, an
optimal D is necessary but insufficient for training stability, or that some aspect of the system results
in D not being trained towards optimality. With the latter possibility in mind, we take a closer look
at the noise in D's spectra in the following section.

[Audio] Their study aims to improve GAN training by extending it to larger models and batches. They use SA-GAN architecture, hinge loss GAN objective function, and conditional batch normalization for class conditioning. They optimize by adding Spectral Norm in the generator, halving the learning rate, and conducting two discriminator steps for every generator step. They evaluate using a moving average of generator weights with a decay rate of 0.9999. Models are initialized orthogonally and trained on Google TPUv3 Pods, with cross-device BatchNorm statistics computation for the generator to enhance performance and scalability.. 

Scene 4

Class
split
ear
4x4x1
ResBlock
ResBlock
Non-local
ResBlock
Image
(a)
Concat
Concat
Upsample
IXI Cmv
BatchNorm
ReLU
Upsatnple
313 conv
BatchNorm
ReLU
3x3 Conv
(b)
IXI Conv
ReLLJ
3x3 conv
3x3 Conv
(c)
Figure 15: (a) A typical architectural layout for BigGAN's G; details are in the following tables.
(b) A Residual Block (ResBlock up) in BigGAN's G. (c) A Residual Block (ResBlock down) in
BigGAN's D.

Conv
Com
(b)
lxi Conv
111 conv
Figure 16: (a) A typical architectural layout for BigGAN-deep's G; details are in the following
tables. (b) A Residual Block (ResBlock up) in BigGAN-deep's G. (c) A Residual Block (ResBlock
down) in BigGAN-deep's D. A ResBlock (without up or down) in BigGAN-deep does not include
the Upsample or Average Pooling layers, and ha_s identity skip connections.

[Audio] Inception Distance and Inception Score measure performance, with lower ID and higher IS indicating better results. The table's first four rows show that increasing batch size boosts performance notably. The article explains that larger batches cover more patterns, enhancing gradients for both generator and discriminator networks.. 

Scene 5

Batch
256
512
1024
2048
2048
92.98 ±4.27
2048
2048
2048
2048
Ch.
64
64
64
64
96
96
96
96
64
Param (M)
81.5
81.5
81.5
81.5
173.5
160.6
158.3
158.3
71.3
Shared Skip-z Ortho.
SA-GAN Baseline
Itr x 10
1000
1000
1000
732
295 +18
185
152
165
371
FID
18.65
15.30
14.88
12.39
9.54 +0.62
9.18 ±0.13
8.51 ±0.32
10.48 ±0.10
52.52
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
58.77
63.03
76.85
94.94
98.76
99.31
86.90
± 1.18
± 1.42
±3.83
± 1.32
±2.84
+2.10
+0.61

Batch
256
512
1024
2048
Ch.
64
64
64
64
Param (M)
81.5
81.5
81.5
81.5
Shared Skip-z Ortho.
SA-GAN Baseline
X
X
X
X
X
X
X
X
X
Itr
1000
1000
1000
732
FID
18.65
15.30
14.88
12.39
52.52
58.77 +1.18
63.03 ±1.42)
76.85 ±3.83

We begin by increasing the batch size for the baseline model, and immediately find tremendous
benefits in doing so. Rows 1-4 of Table 1 show that simply increasing the batch size by a factor of
8 improves the state-of-the-art IS by 46%. We conjecture that this is a result of each batch covering
more modes, providing better gradients for both networks. One notable side effect of this scaling is
that our models reach better final performance in fewer iterations, but become unstable and undergo
complete training collapse. We discuss the causes and ramifications of this in Section 4. For these
experiments, we report scores from checkpoints saved just before collapse.

[Audio] Furthermore, another advantage of increasing the batch size is the reduction in training time required to achieve the specified performance. For example, in the table, for a batch size of 256, FID reaches 18.65 after iterating 1000*10^3 times, whereas for a batch size of 2048, it only takes 732*10^3 iterations to reach an ID of 12.39. 

Scene 6

2048
64
64
81.5
81.5
X
X
X
X
X
X
1000
732
12.39
76.85 ±3.83

[Audio] However, is increasing the batch size always better? Certainly not. The article mentions that for larger batch sizes, the model training becomes more unstable and prone to mode collapse, where the generator output becomes singular or similar for different latent variables.. 

Scene 7

[Audio] The authors boosted the width of each layer in both the generator and discriminator by 50%, leading to a 21% increase in IS performance as evident from rows 4 and 5 of the table. They propose that augmenting network complexity can bolster performance, particularly for intricate datasets. However, despite this adjustment, adding more layers did not yield any performance enhancement.. 

Scene 8

2048
2048
64
96
81.5
173.5
X
X
X
X
X
X
732
295
12.39
9.54 ±0.62
92.98
±4.27)

We then increase the width (number of channels) in each layer by 50%, approximately doubling the
number of parameters in both models. This leads to a further IS improvement of 21%, which we
posit is due to the increased capacity of the model relative to the complexity of the dataset. Doubling

[Audio] Previous GAN models typically utilized standard normal or uniform distributions for the latent space prior. However, this may not be ideal. The Truncation Trick, introduced by the authors, addresses this issue by enabling sampling with a different latent distribution, improving sample quality. It balances individual sample quality and overall diversity but diminishes diversity as the threshold decreases, causing elements of the latent space to converge toward the mode of the generator's output distribution.. 

Scene 9

(a)
(b)
Figure 2: (a) The effects of increasing truncation. From left to right, the threshold is set to 2, 1, 0.5,
0.04. (b) Saturation artifacts from applying truncation to a poorly conditioned model.

[Audio] The authors introduce orthogonal regularization to enhance model adaptability to the Truncation Trick, allowing fine-tuning between sample quality and diversity. By computing FID and IS across different thresholds, they generate a variety-fidelity curve akin to a precision-recall curve. IS increases with lower truncation thresholds, resembling precision, while FID initially improves but sharply drops as truncation nears zero, resembling recall. However, some larger models exhibit saturation artifacts when truncated noise is used. To address this, they enforce amenability to truncation by conditioning the generator to be smooth through Orthogonal Regularization. This regularization, governed by a hyperparameter β, ensures orthogonality among weight matrices. Several relaxed variants are explored, with the best-performing version removing diagonal terms and minimizing pairwise cosine similarity between filters. A small penalty of 10^-4 on β improves the likelihood of model amenability to truncation, with models trained using Orthogonal Regularization showing a 60% amenability rate compared to 16% without it.. 

Scene 10

(a)
(b)
Figure 2: (a) The effects of increasing truncation. From left to right, the threshold is set to 2, I, 0.5,
0.04. (b) Saturation artifacts from applying truncation to a poorly conditioned model.

Batch
256
512
1024
2048
2048
92.98 ±4.27
2048
2048
2048
Ch.
64
64
64
64
96
96
96
96
64
Param (M)
81.5
81.5
81.5
81.5
173.5
160.6
158.3
158.3
71.3
Shared
Skip-z Ortho.
SA-GAN Baseline
X
X
X
X
Itr XIO
1000
1000
1000
732
295
185
152 ±7
165 +13
371
FID
18.65
15.30
14.88
12.39
9.54 ±0.62
9.18 ±0.13
8.73 ±0.45
8.51 ±0.32
10.48 +0.10
52.52
58.77 ±1.18
X
X
X
X
X
X
X
X
X
X
X
76.85
94.94
98.76
99.31
86.90
+3.83
± 1.32
+2.84
+2.10

[Audio] Previous research has mostly looked at GAN stability on small-scale problems, but the issues we're seeing arise on larger scales. To understand these instabilities, we track various statistics during training. They found that analyzing the top three singular values of each weight matrix is most helpful. These values can be calculated efficiently using a method called Arnoldi iteration. They notice that most layers in the generator behave well, but some, especially the first layer, become unstable during training.. 

Scene 11

APPENDIX F
MONITORED TRAINING STATISTICS
Ill
(h) D 02
Figure 20: Training statistics for a typical mcxlel without special modifications.
after 200000 iterations.
Collapse crcurs

[Audio] To see if this instability causes the collapse of training or is just a symptom, we experiment with adding extra constraints to generator to counteract the instability. They try different methods like regularizing the top singular values of each weight or clamping them to certain values. However, these techniques only partially improve stability and don't prevent training collapse. So, we shift our focus to studying the discriminator.. 

Scene 12

Figure 21:
iterations.
(a) ao
(c) 01
G training statistics with in G regularized towards l.
Iteration
(d) 02
Collapse occurs after 125000

[Audio] The discriminator's weight spectra show noise but remain stable overall, with singular values increasing steadily. Spikes occur during collapse, suggesting periodic large gradients from strong generator perturbations.. 

Scene 13

4.2 CHARACTERIZING INSTABILITY: THE DISCRIMINATOR
As with G, we analyze the spectra of D's weights to gain insight into its behavior, then seek to
stabilize training by imposing additional constraints. Figure 3(b) displays a typical plot of ao for D
(with further plots in Appendix F). Unlike G, we see that the spectra are noisy, is well-behaved,
and the singular values grow throughout training but only jump at collapse, instead of exploding.
The spikes in D's spectra might suggest that it periodically receives very large gradients, but we
observe that the Frobenius norms are smooth (Appendix F), suggesting that this effect is primarily
concentrated on the top few singular directions. We posit that this noise is a result of optimization
through the adversarial training process, where G periodically produces batches which strongly per-
turb D . If this spectral noise is causally related to instability, a natural counter is to employ gradient
penalties, which explicitly regularize changes in D's Jacobian. We explore the RI zero-centered
gradient penalty from Mescheder et al. (2018):
(5)
With the default suggested strength of 10, training becomes stable and improves the smoothness
and boundedness of spectra in both G and D, but performance severely degrades, resulting in a 45%
reduction in IS. Reducing the penalty partially alleviates this degradation, but results in increasingly
ill-behaved spectra; even with the penalty strength reduced to 1 (the lowest strength for which sud-
den collapse does not occur) the IS is reduced by 20%. Repeating this experiment with various
strengths of Orthogonal Regularization, (Srivastava et al., 2014), and L2 (See Appendix I
for details), reveals similar behaviors for these regularization strategies: with high enough penalties
on D, training stability can be achieved, but at a substantial cost to performance.
We also observe that D's loss approaches zero during training, but undergoes a sharp upward jump at
collapse (Appendix F). One possible explanation for this behavior is that D is overfitting to the train-
ing set, memorizing training examples rather than learning some meaningful boundary between real
and generated images. As a simple test for D's memorization (related to Gulrajani et al. (2017)), we
evaluate uncollapsed discriminators on the ImageNet training and validation sets, and measure what
percentage of samples are classified as real or generated. While the training accuracy is consistently
above 98%, the validation accuracy falls in the range of 50-55%, no better than random guessing
(regardless of regularization strategy). This confirms that D is indeed memorizing the training set;
we deem this in line with D's role, which is not explicitly to generalize, but to distill the training
data and provide a useful learning signal for G. Additional experiments and discussion are provided
in Appendix G.

[Audio] R1 gradient penalty was added for stability, but it hurts performance. Even lowering it still harms performance. The discriminator's loss drops drastically during training, then spikes at collapse, indicating overfitting. Testing on ImageNet confirms this, with the discriminator excelling on training data but performing no better than chance on validation. It suggests the discriminator prioritizes giving feedback to the generator over generalizing.. 

Scene 14

Iteration
(d) 02
Figure 24: D training statistics with an RI Gradient Penalty of strength 10 on D. This model does
not collapse, but only reaches a maximum IS of 55.

[Audio] Stabilizing the discriminator compromises performance as it tends to memorize rather than generalize. Training stability relies on the interaction between the generator and discriminator. Although constraining the discriminator aids stability, it harms performance. The optimal strategy is to ease constraints, enabling training until later stages when pattern collapse risks arise.. 

Scene 15

[Audio] This paper demonstrates the advantages of scaling up GANs for modeling natural images. It improves fidelity and diversity in generated samples, outperforming existing ImageNet GAN models. The research examines large-scale GAN training, focusing on stability measured by weight matrix singular values, uncovering insights into stability-performance dynamics. Notably, the experiment produces 512-pixel images using 512 Google TPUs over 48 hours, presenting replication challenges.. 

Scene 16

Scaling up Generative Adversarial Networks (GANs) for modeling natural images across various categories brings significant benefits. It improves both the fidelity and diversity of generated samples. The models introduced in this study outperform existing ImageNet GAN models by a considerable margin. The analysis of large-scale GAN training behavior focuses on stability, particularly characterized by the singular values of weight matrices. This analysis highlights the relationship between stability and performance in GAN training. Challenging to reproduce

We use an exponential moving average of the weights of G at sampling time, with a decay rate set to
0.9999. We employ cross-replica BatchNorm (Ioffe & Szegedy, 2015) in G, where batch statistics are
aggregated across all devices, rather than a single device as in standard implementations. Spectral
Normalization (Miyato et al., 2018) is used in both G and D, following SA-GAN (Zhang et al., 2018).
We train on a Google TPU v3 Pod, with the number of cores proportional to the resolution: 128 for
128, 256 for 256x256, and 512 for 512x512. Training takes between 24 and 48 hours for
most models. We increase e from the default 10¯8 to in BatchNorm and Spectral Norm to
mollify low-precision numerical issues. We preprocess data by cropping along the long edge and
rescaling to a given resolution with area resampling.

Hi
 I am presenting this article named large SCALE GAN TRAINING For H-I-G-H fidelity natural IMAGE SYNTHESIS. This paper was an oral presentation at ICLR 2019. The team consisted of three researchers: Brock from Heriot-Watt University
 Donahue and Simonyan from Google DeepMind.

avatar

Recent advancements in image generation struggle to produce high-resolution
 diverse samples from datasets like ImageNet. Generative Adversarial Networks (GANs) offer promise but face challenges in stability and performance. Researchers propose a solution: applying orthogonal regularization to the generator
 allowing control over sample fidelity and diversity via latent space truncation. This modification achieves state-of-the-art results in class-conditioned image synthesis on ImageNet
 addressing instability challenges inherent in large-scale G-A-N training.

The study finds that stability in training doesn't stem solely from the generator or discriminator but emerges from their interaction. Symptoms of pathological conditions can be tracked
 showing the need for reasonable training conditions. Tight constraints on the discriminator can ensure stability but harm sample quality. Loosening this condition post satisfactory training can improve data generation effectiveness.

Their study aims to improve G-A-N training by extending it to larger models and batches. They use SA-GAN architecture
 hinge loss G-A-N objective function
 and conditional batch normalization for class conditioning. They optimize by adding Spectral Norm in the generator
 halving the learning rate
 and conducting two discriminator steps for every generator step. They evaluate using a moving average of generator weights with a decay rate of 0.9999. Models are initialized orthogonally and trained on Google TPUv3 Pods
 with cross-device BatchNorm statistics computation for the generator to enhance performance and scalability.

Inception Distance and Inception Score measure performance
 with lower ID and higher IS indicating better results. The table's first four rows show that increasing batch size boosts performance notably. The article explains that larger batches cover more patterns
 enhancing gradients for both generator and discriminator networks.

Furthermore
 another advantage of increasing the batch size is the reduction in training time required to achieve the specified performance. For example
 in the table
 for a batch size of 256
 F-I-D reaches 18.65 after iterating 1000*10^3 times
 whereas for a batch size of 2048
 it only takes 732*10^3 iterations to reach an ID of 12.39

However
 is increasing the batch size always better? Certainly not. The article mentions that for larger batch sizes
 the model training becomes more unstable and prone to mode collapse
 where the generator output becomes singular or similar for different latent variables.

The authors boosted the width of each layer in both the generator and discriminator by 50%
 leading to a 21% increase in IS performance as evident from rows 4 and 5 of the table. They propose that augmenting network complexity can bolster performance
 particularly for intricate datasets. However
 despite this adjustment
 adding more layers did not yield any performance enhancement.

Previous G-A-N models typically utilized standard normal or uniform distributions for the latent space prior. However
 this may not be ideal. The Truncation Trick
 introduced by the authors
 addresses this issue by enabling sampling with a different latent distribution
 improving sample quality. It balances individual sample quality and overall diversity but diminishes diversity as the threshold decreases
 causing elements of the latent space to converge toward the mode of the generator's output distribution.

The authors introduce orthogonal regularization to enhance model adaptability to the Truncation Trick
 allowing fine-tuning between sample quality and diversity. By computing F-I-D and IS across different thresholds
 they generate a variety-fidelity curve akin to a precision-recall curve. IS increases with lower truncation thresholds
 resembling precision
 while F-I-D initially improves but sharply drops as truncation nears zero
 resembling recall. However
 some larger models exhibit saturation artifacts when truncated noise is used. To address this
 they enforce amenability to truncation by conditioning the generator to be smooth through Orthogonal Regularization. This regularization
 governed by a hyperparameter β
 ensures orthogonality among weight matrices. Several relaxed variants are explored
 with the best-performing version removing diagonal terms and minimizing pairwise cosine similarity between filters. A small penalty of 10^-4 on β improves the likelihood of model amenability to truncation
 with models trained using Orthogonal Regularization showing a 60% amenability rate compared to 16% without it.

Previous research has mostly looked at G-A-N stability on small-scale problems
 but the issues we're seeing arise on larger scales. To understand these instabilities
 we track various statistics during training. They found that analyzing the top three singular values of each weight matrix is most helpful. These values can be calculated efficiently using a method called Arnoldi iteration. They notice that most layers in the generator behave well
 but some
 especially the first layer
 become unstable during training.

To see if this instability causes the collapse of training or is just a symptom
 we experiment with adding extra constraints to generator to counteract the instability. They try different methods like regularizing the top singular values of each weight or clamping them to certain values. However
 these techniques only partially improve stability and don't prevent training collapse. So
 we shift our focus to studying the discriminator.

The discriminator's weight spectra show noise but remain stable overall
 with singular values increasing steadily. Spikes occur during collapse
 suggesting periodic large gradients from strong generator perturbations.

R1 gradient penalty was added for stability
 but it hurts performance. Even lowering it still harms performance. The discriminator's loss drops drastically during training
 then spikes at collapse
 indicating overfitting. Testing on ImageNet confirms this
 with the discriminator excelling on training data but performing no better than chance on validation. It suggests the discriminator prioritizes giving feedback to the generator over generalizing.

Stabilizing the discriminator compromises performance as it tends to memorize rather than generalize. Training stability relies on the interaction between the generator and discriminator. Although constraining the discriminator aids stability
 it harms performance. The optimal strategy is to ease constraints
 enabling training until later stages when pattern collapse risks arise.

This paper demonstrates the advantages of scaling up G-A-N-s for modeling natural images. It improves fidelity and diversity in generated samples
 outperforming existing ImageNet G-A-N models. The research examines large-scale G-A-N training
 focusing on stability measured by weight matrix singular values
 uncovering insights into stability-performance dynamics. Notably
 the experiment produces 512-pixel images using 512 Google T-P-U-s over 48 hours
 presenting replication challenges.