C OLLAPSE BY C ONDITIONING : T RAINING C LASSCONDITIONAL GAN S WITH L IMITED D ATA
Anonymous authors
Paper under double-blind review
A BSTRACT
Class-conditioning offers a direct means of controlling a Generative Adversarial
Network (GAN) based on a discrete input variable.
While necessary in many applications, the additional information provided by the class labels could even be expected to beneﬁt the training of the GAN itself.
Contrary to this belief, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.
Motivated by this observation, we propose a training strategy for conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning.
Our training strategy starts with an unconditional GAN and gradually injects conditional information into the generator and the objective function.
The proposed method for training cGANs with limited data results not only in stable training but also in generating high-quality images, thanks to the early-stage exploitation of the shared information across classes.
We analyze the aforementioned mode collapse problem in comprehensive experiments on four datasets.
Our approach demonstrates outstanding results compared with state-of-the-art methods and established baselines.
I NTRODUCTION
Since the introduction of generative adversarial networks (GANs) by Goodfellow et al.
(2014), there has been a substantial progress in realistic image and video generations.
The contents of such generations are often controlled by conditioning the process by means of conditional GANs (cGANS)
(Mirza & Osindero, 2014).
In practice, cGANs are of high interest, as they can generate and control a wide variety of outputs using a single model.
Some example applications of cGANs include; category-conditioned generation (Brock et al., 2018), image manipulation (Yu et al., 2018), image-to-image translation (Zhu et al., 2017), and text-to-image generation (Xu et al., 2018).
Despite the remarkable success, training cGANs requires large training data, including conditioning labels, for realistic generation and stable training behavior (Tseng et al., 2021).
Collecting large enough data is challenging in many frequent scenarios, due to the privacy, the quality and the diversity required, among other reasons.
This difﬁculty is often worsened further for datasets for conditional training, where also labels need to be collected.
The case of ﬁne-grained conditioning adds an additional challenge for data collection, since the availability of the data samples and their variability are expected to deteriorate with the increasingly ﬁne-grained details (Gupta et al., 2019).
While training GANs with limited data has recently received some attention (Karras et al., 2020a;
Wang et al., 2018; Tseng et al., 2021), the inﬂuence of conditioning in this setting remains unexplored.
Compared to the unconditional case, the conditional information provides additional supervision and input to the generator.
Intuitively, this additional information can guide the generation process better and ensure the success of cGANs whenever its unconditional counterpart succeeds.
In fact, one may even argue that the additional supervision by conditioning can even alleviate the problem of limited data, to an extent.
Surprisingly however, we observe an opposite effect (to our preliminary intuition) in our experiments for class-conditional GANs.
As visualized in Fig.
1, the conditional GAN trained on limited data suffers from severe mode collapse.
Its unconditional counterpart, on the other hand, trained on the same data, is able to generate diverse images of high ﬁdelity with stable training process.
To our knowledge, these counter-intuitive observations of cGANs have not been observed or reported in previous works.
U n c o nd i t i o n a l
ImageNet Carnivores
Food
CUB-200AnimalFace
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In this paper, we ﬁrst study the behaviour of a state-of-the-art class-conditional GAN with varying number of classes and image samples per class, and contrast it to the unconditional case.
Our study in the limited data regime reveals that the unconditional GANs compare favorable with conditional ones, in terms of generated image quality.
We, however, are interested in the conditional case, so as to be able to control the image generation process using a single generative model.
In this work, we therefore set out to mitigate the aforementioned mode collapse problem.
Motivated by our experimental observations, we propose a method for training cGANs that leverages the stable training of the unconditional GANs.
During the training process, we integrate a gradual transition from unconditional to conditional generative learning.
The early stage of the proposed training method favours the unconditional objective for the sake of stability, whereas the later stage favours the conditional objective for the desired control over the output by conditioning.
Our transitional training procedure only requires minimal changes in existing state-of-the-art generator and discriminator architectures.
We demonstrate the advantage of the proposed method over the existing ones, by evaluating our method on four benchmark datasets under the limited data setups.
The major contributions of this study are summarized as follows:
• We identify and characterize the problem of conditioning-induced mode collapse when training class-conditional GANs under limited data setups.
• We propose a training method for cGANs that exploits the training stability of unconditional GANs to mitigate the observed conditioning collapse.
• The effectiveness of the proposed method is demonstrated on four benchmark datasets.
The method is shown to signiﬁcantly outperform the state-of-the art and the compared baselines.
[Figure]
C LASS - CONDITIONING M ODE C OLLAPSE
Learning conditional image generation networks is becoming an increasingly important task.
The ability to control the generator is a fundamental feature in many applications.
However, even in the context of unconditional GANs, previous studies suggest that class information as extra supervision can be used to improve the generated image quality (Salimans et al., 2016; Zhou et al., 2018;
Kavalerov et al., 2020).
This, in turn, may set an expectation that, the extra supervision by conditioning must not lead to the mode collapse of cGANs in setups where the unconditional GANs succeed.
Furthermore, one may also expect to resolve the issue of training cGANs on limited data, to an extent, due to the availability of the additional conditional labels.
As the ﬁrst part of our study, we investigate the effect of conditioning on GANs under the limited data setup.
We base our experiments on StyleGAN2 with adaptive data augmentation (ADA), which is a recent state-of-the-art method for unconditional and class conditional image generation under limited-data setup (Karras et al., 2020a).
Both unconditional and conditional versions of StyleGAN are trained on four benchmark datasets (more details in Section 4.1), with the setup of this paper.
The selected datasets are somewhat of ﬁne-grained in nature, where the problem of limited data, concerning their availability and labelling difﬁculty, is often expected to be encountered.
In Fig.
1, we analyze the training of conditional and unconditional GANs by plotting the Fr´echet inception distance (FID) (Heusel et al., 2017) during training.
The analysis is performed on four different datasets, each containing between 1170 and 2410 images for training.
Contrary to our initial expectation, the conditional version obtains consistently worse FID during training, compared to the unconditional training.
To analyze its cause, in Fig.
1 we also visualize samples from the best model attained during training in terms of FID.
For each dataset, the unconditional model learns to generate diverse and realistic images, while lacking the ability to perform class-conditional generation.
On the other hand, the conditional model suffers from severe mode collapse.
Speciﬁcally, the intra-class variations are very small and mainly limited to color changes, while retaining the same structure and pose.
Moreover, the images lack realism and contain pronounced artifacts.
Next, we further characterize the mode collapse problem observed in Fig.
1 by analyzing its dependence on the size of the training dataset.
To this end, we employ the ImageNet Carnivores c
G
Style-mapping Network
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ng s λ t λ t dataset (Liu et al., 2019), which includes a larger number of classes and images.
We perform the analysis by gradually reducing the size of the training set in two ways.
In Fig.
2a, we reduce the number of classes while keeping a number of 100 training images per class.
In Fig.
2b, we reduce the number of images per class while using a 50 classes in all cases.
In both cases, the conditional
GAN achieves a better FID for larger datasets, here above 5k images.
This observation is in line with previous work (Salimans et al., 2016;
Zhou et al., 2018; Kavalerov et al., 2020).
However, when reducing the dataset size, the order is inverted.
Instead, the unconditional model achieves consistently better FID, while the conditional model degrades rapidly.
Inspired by these observations, we set out to design a training strategy for conditional GANs that eliminates the mode collapse induced by the conditioning itself.
Our proposed approach, presented next, achieves stable training that attains low FID, as visualized in Fig.
The images generated from our model exhibit natural intra-class variations with substantial changes in pose, appearance, and color.
Furthermore, our training strategy outperforms both the conditional and unconditional models in terms of FID in a wide range of dataset sizes, as shown in Fig.
M ETHOD 3. F ROM U NCONDITIONAL TO C ONDITIONAL GAN S
As the analysis in Section 2 reveals, conditional GAN training leads to mode collapse when training data is limited, while the unconditional counterpart achieves good performance for the same number of training samples.
This discovery motivates us to design an approach capable of leveraging the advantages of unconditional training in order to prevent mode-collapse in conditional GANs.
Our initial inspections of the generated images during conditional training indicated that the mode collapse appears from the very early stages of the training.
On the contrary, the corresponding unconditional GAN learns to generate diverse images with gradually improved photo-realism during training.
In order to avoid mode-collapse, we aim to exploit unconditional learning in the beginning of the training process.
Unconditional training in early stages allows the GAN model to learn the distribution of the real images without the complications caused by conditioning.
We then introduce the conditioning in the later stages of the training.
This allows the network to adapt to conditional generation in a more stable way, by exploiting the partially learned data manifold.
In general, we control the transition between unconditional and conditional generative learning during the training process with a function λ t ≥ 0 .
The subscript t denotes the iteration number during training.
Speciﬁcally, a zero value λ t = 0 implies a purely unconditional learning, i.e. the conditional information does not affect the generator or discriminator networks.
Our goal is to design a training strategy capable of gradually incorporating conditioning during training by increasing the value of λ t .
While any monotonically increasing function λ t may be chosen, we only consider a simple linear transition from 0 to 1 as,
[Equation]
Here, T s and T e denote the start and end time steps of the transition, respectively.
We achieve the desired transition during training by introducing a method for controlling the behavior of the generator and discriminator using the function λ t .
An overview of our approach, detailed in next sections, is illustrated in Fig.
3. T RANSITION IN THE G ENERATOR
The generator G ( z ) of a GAN is trained to map the latent vector z , drawn from a prior distribution p ( z ) , to a generated image I g
= G ( z ) belonging to the distribution of the training data.
A conditional generator G ( z, c ) additionally receives the conditioning variable c as input, aiming to learn the image distribution of the training data conditioned on c .
The transition from an unconditional G ( z ) to a conditional G ( z, c ) generator seemingly requires a discrete architectural change during the training process.
We circumvent this by additionally conditioning our generator on the transition function λ t as G ( z, c, λ t ) .
More speciﬁcally, we gradually incorporate conditional information during training by using a generator of the form,
[Equation]
Here, S and E are neural network modules that transform the latent and conditional vector respectively.
In case of λ t = 0 , the conditional information is masked out, leading to a purely unconditional generator G ( S ( z )) .
During the transition step T s < t < T e , the importance of the conditional information is gradually increased during training.
To achieve the generator in equation 2, we perform a minimal modiﬁcation to the class-conditional
StyleGAN2 (Karras et al., 2020b) baseline.
The generator consist of a style-mapping network and an image synthesis network.
The style-mapping network maps the input latent vector z and the embedding of the condition c to the intermediate representation w , known as style code.
The image synthesis network, then generates images from the style codes.
As illustrated in Fig.
3, we modify the conditioning in the generator by feeding the class embeddings to a fully-connected layer, which is then weighted by λ t before adding to the output of the style-mapping network’s ﬁrst layer.
3. T RANSITION IN THE TRAINING OBJECTIVE
The unconditional and conditional GAN frameworks also differ in their training objectives.
The latter objective assesses the realism of an image based on its conditioning variable.
We propose using both unconditional and conditional objectives and follow the same transition as in the generator.
Our proposed loss function includes the unconditional objective during the whole training.
The conditional term, on the other hand, is weighted by the transition function λ t .
Our total objective is thus given by,
[Equation]
Here, L Duc , L Dc , and L D are the unconditional, conditional, and the proposed losses for the discriminator respectively.
Similarly, L Guc , L Gc , and L G represent the unconditional, conditional, and the proposed losses for the generator.
As the discriminator is responsible to predict the scores needed for calculating the loss, the proposed training objective requires also modifying the discriminator’s architecture.
In this regard, the necessary modiﬁcation must provide both conditional and unconditional scores.
We propose using two prediction branches, separately dedicated to conditional and unconditional cases, in the last layer of the discriminator, as shown in Fig.
The proposed training objective and discriminator for StyleGAN2 are also visualized in Fig.
In the discriminator of StyleGAN2, conditioning is performed by matching the features of the input images with the target class embedding.
In other words, the conditional discriminator assigns scores to the input images by calculating the dot-product between the features of the input images and the embedding of the target class c .
The proposed discriminator architecture bears some resemblance to the architecture of projection discriminators (Miyato & Koyama (2018)).
In contrast to our approach, the projection discriminator aggregates the unconditional and conditional scores inside the discriminator before the loss function.
Additionally, the projection discriminator does not perform any transition between the two scores.
E XPERIMENTS
In this section, we ﬁrst provide the details of our experimental setup.
Then, we present the quantitative and qualitative results of the proposed method, as well as the comparison with existing methods.
Finally, we provide more ablation and analysis of different components of our method.
4. E XPERIMENT S ETUP
Datasets: We use four datasets to evaluate our method: ImageNet Carnivores (Liu et al., 2019),
CUB-200-2011 (Wah et al., 2011), Food101 (Bossard et al., 2014), and AnimalFace (Si & Zhu, 2011).
To keep our experiments in the limited-data regime, we decrease the number of classes and images per class in some of these datasets using random sampling.
• ImageNet Carnivores is a subset of the ImageNet dataset (Deng et al., 2009), which contains 149 classes of carnivore animals.
The images were processed by the authors to only contain the faces of the animals.
We use a subset of the dataset with 20 classes andimages per class.
• Food101 contains 101 different food categories, having a total amount of 101k images.
We use a subset of the dataset with 20 classes and 100 images per class.
• CUB-200-2011 contains 200 categories of different bird spices with only around 60 images per class.
We use a subset of this dataset with 20 classes with all the images in each class.
• AnimalFace , similar to ImageNet Carnivores, is a dataset that contains images of animal faces.
However, the animal in AnimalFace are not limited to carnivores.
AnimalFace contains 20 classes with 2432 images in total.
Since AnimalFace is already a small dataset, we do not further reduce its size.
Implementation details: We base our method on the ofﬁcial PyTorch implementation of StyleGAN2+ADA.
The hyper-parameter selection for the base unconditional and conditional StyleGAN is performed automatically as provided in the ofﬁcial implementation.
Training is done with a batch size of 64 spread over 4 GPUs.
For the transition function we use T s = 2 k and T e = 4 k .
Evaluation Metrics: We evaluate our method using Fr ´ echet inception distance (FID) as the mostcommonly used metric for measuring the similarity between the distribution of real and generated images.
As FID can be biased when real data is small (Karras et al., 2020a), we also include kernel inception distance (KID)
(Bi ´ nkowski et al., 2018) as a metric which is unbiased by design.
4. R ESULTS AND C OMPARISONS
To assess the efﬁcacy of the proposed method, we provide a quantitative comparison with the wellestablished baselines and existing methods:
• BigGAN+ADA (Brock et al., 2018) :
Achieving outstanding results on ImageNet, BigGAN has been widely used for class-conditional image generation (more details in Section 5).
We use the implementation with ADA provided by (Kang & Park, 2020).
• ContraGAN+ADA (Kang & Park, 2020) :
A class-conditional model based on BigGAN that ouperforms BigGAN in many setups using self-supervision in the discriminator (more details in Section 5).
We use the implementation provided by the authors.
• U-StyleGAN+ADA:
Unconditional training of StyleGAN2+ADA using the original unconditional architecture.
• C-StyleGAN+ADA:
Conditional training of StyleGAN2+ADA using the original conditional architecture.
• C-StyleGAN+ADA+projD: Modiﬁed Conditional StyleGAN2+ADA by replacing the discriminator with projection discriminator (Miyato & Koyama, 2018).
• C-StyleGAN+ADA+Lecam:
Regularizing C-StyleGAN2+ADA using Lecam regularizer, recently proposed by Tseng et al.
(2021) for limited-data setup (more details in Section 5).
The authors have
suggested a hyper-parameter in the range of [0 . 1 , 0 . 5]
As we
did
not observe a noticeable difference between different values in the suggested range
, we set
the hyper-parameter to 0.3 for all experiments
Results are reported in Table 1.
BigGAN and ContraGAN, even though coupled with ADA, struggle to achieve any good generation quality in our experiments.
Conditional StyleGAN2 shows better results on three of the datasets compared to that of the other two conditional competitors.
However, the FID and KID scores are still very high.
As discussed before, C-StyleGAN is consistently outperformed by its unconditional counterpart.
Replacing the StyleGAN2’s discriminator with projection discriminator does not yield a noticeable advantage over the original architecture, as it brings improvements on two of the datasets, but degrades the performance on the other two.
Adding Lecam regularization to the C-StyleGAN2 shows promising results, achieving good FID and KID scores on Food101 and AnimalFace.
However, it still fails to achieve as good generation quality as the unconditional StyleGAN2.
The FID and KID scores for the proposed method indicate signiﬁcant and consistent advantage over all the compared methods in all four datasets.
Our method is able to maintain a stable training and achieve better generation quality than both unconditional and conditional StyleGAN2.
The FID curves during training along with example generated images using the
[Table]
proposed method are visualized in Fig.
FID curves indicate training dynamics as stable as the unconditional training
[Table]
while achieving better FID.
In addition, the generated images of our method are clearly of more diversity and quality compared to those of the standard conditional model, showing the advantage of the proposed method.
The results in Fig.
2 further demonstrate a clear advantage of our method over a wide range of dataset sizes.
Our approach maintains the performance of cGANs for larger datasets
[Table]
while signiﬁcantly outperforming the unconditional counterpart when data is more scarce.
This shows that our method enables cGANs to use the additional information provided by the labels to achieve better generation quality without falling into any mode collapse induced by the conditioning
[Table]
4. A BLATION AND A NALYSIS
In this section
[Table]
we provide further ablation and analysis over different components of our method.
First
[Table]
we provide an ablation study containing four different variants:
• No transition: Training the modiﬁed architecture with the new objective, without any transition in the objective or the generator (equivalent to using an auxiliary unconditional loss term to train a conditional model).
• Transition only in G:
Performing the transition only in the generator (Sec. 3.2), while the training objective is the summation of the unconditional and conditional term.
• Transition only in Loss:
Performing the transition only in the training objective (Sec. 3.3), while the generator is fully conditional from the beginning.
• Final method: The ﬁnal method with all the proposed components.
[Table]
Table 2 presents the results of the ablation study on Food101 and ImageNet Carnivores.
The No transition version yields poor results, showing that the mode collapse is not alleviated by only adding an auxiliary unconditional training objective.
Adding the transition to the generator already brings signiﬁcant improvement to the model.
Having the transition only in the objective, on the other, does not lead to good results.
To our initial surprise
[Table]
this reveals that transitioning in the generator is a crucial part of the method.
However, also having the transition in the objective in addition to that in the generator, corresponding to our ﬁnal method, achieves the best results
[Table]
Next, we analyze the impact of when the transition between unconditional and conditional learning is applied.
The total transition time is ﬁxed to T e − T s
= 2 k time steps.
We then report the results on the Food101 and ImageNet
Carnivores datasets for different starting times
T s in Table 3.
Importantly, we notice a significantly worse results if the transition is started in the beginning of the training T s = 0 .
This further supports the hypothesis that conditioning leads to mode collapse in the early stages of the training.
By introducing conditional information in a later stage, good FID and KID numbers are obtained without being sensitive to the speciﬁc choice of T s .
In Table 4, we further independently analyze the transition end time T e , while keeping T s = 2 k ﬁxed.
Again, our approach is not sensitive to its value.
Our method does thus not require extensive hyper-parameter tuning.
[Table]
Lastly, we visualize on evolution of the generated images and formation of classes during our training process.
Fig 4 shows how different images generated from the unconditional phase of training on AnimalFace all start to evolve into different images of the class Panda.
In addition to the formation of the classes, Fig. 4 also shows how the image quality continues to improve during and after the transition.
In the
Appendix, randomly generated images from all classes of all datasets are provided for more detailed assessment of the ability of the proposed method in conditional high-quality image generation.
R ELATED W ORK
Class-conditional GANs: The ﬁrst conditional GAN architecture, introduced by Mirza & Osindero (2014), incorporated conditioning by concatenating the condition variable to the input of the generator and the discriminator.
AC-GAN (Odena et al., 2017) equipped the discriminator with an auxiliary classiﬁcation task to ensure the conditional generation.
cGAN with projection discriminator (Miyato & Koyama, 2018) replaced the two-headed discriminator of AC-GAN with a single-output discriminator that outputs the sum of the realism score and class score.
The class score is calculated using the dot-product of the discriminator features and the target class embedding.
The conditioning in the generator, on the other hand, is performed through the conditional batch normalization layers.
Improving over cGAN with projection discriminator, BigGAN (Brock et al., 2018) was able to become the state-of-the-art cGAN on ImageNet dataset (Deng et al., 2009).
Inspired by the recent progress in self-supervised learning, ContraGAN (Kang & Park, 2020) improved over BigGAN by exploiting an auxiliary self-supervised task in the discriminator for better image representation learning.
StyleGAN (Karras et al., 2019), mainly known for unconditional image generation, was extended to t =t =t =t =t =
t =t = class conditioning in the improved version, and coupled with adaptive differentiable augmentation, outperformed the state-of-the-art cGANs in small data setup (Karras et al., 2020a).
cGANs in small data regime: There are several directions for addressing the problem of training
GANs on small datasets.
Transfer learning is one of these directions that exploits a large pretraining dataset to provide a better initialization for learning the target data.
In recent years several works have studied the best practices for performing transfer learning in GANs (Wang et al., 2018; 2020;
Mo et al., 2020; Zhao et al., 2020a; Noguchi & Harada, 2019)).
As an example of transfer learning in the context of conditional GANs, cGANTransfer (Shahbazi et al., 2021) proposed class-speciﬁc knowledge transfer from source dataset to the target by learning to combine the source class representations, which embedded as the parameters of the batch normalization layers.
Although transfer learning is an effective technique for training GANs on small datasets, it usually requires access to a large pretraining dataset with sufﬁcient domain relevance to the target data.
Data augmentation is another technique for tackling the problem of small data.
In order to avoid augmentations from leaking to the generated images, recent concurrent works proposed differentiable data augmentation Zhao et al.
(2020b);
Karras et al.
(2020a).
Including adaptive differentiable augmentation (ADA) as a part of StyleGAN2 resulted in a signiﬁcant improvement in conditional image generation quality and training stability on small datasets, outperforming previous conditional GANs on cifar10 dataset (Karras et al., 2020a).
In addition to the aforementioned techniques, there are other studies on GANs in small data regime focusing on better architecture or loss function design.
Liu et al.
(2021) proposed a lighter unconditional architecture for StyleGAN for faster training, as well as a self-supervised discriminator to increase the stability on fewer samples.
As another approach,
Tseng et al.
(2021) proposed a regularization to prevent the discriminator from over-ﬁtting in smalldata setup by penalizing the difference between the predictions of the discriminator for current real images and those of previous generated images, as well as the difference between the predictions for current generated images and those of previous real images.
C ONCLUSIONS
In this work, we studied the problem of training class-conditional Generative Adversarial Networks (cGANs) with limited data.
Our empirical study demonstrated that class-conditioning can lead the training of GANs to mode-collapse within the investigated setup.
To prevent from such collapse, we presented a method of injecting the class conditioning by transitioning from unconditional to conditional case, in an incremental manner.
To enable such transition, we have proposed architectural modiﬁcations and training objects, which can be easily adapted by any existing GAN models.
The proposed method achieves outstanding results when compared with the state-of-the-art methods and established baselines, for the limited data setup of four benchmark datasets.
In future, we will test and study our method for the cases of conditioning beyond object classes (e.g. semantics).
A A PPENDIX
In the following, we provide more experiments and visualizations on the discovered observation and the proposed method.
A. S TYLE GAN2 WITHOUT ADA
To investigate whether the appearance of the conditional collapse and efﬁcacy of the proposed method in solving it is related to the adaptive differentiable augmentation (ADA), we perform an experiment on a subset of ImageNet carnivores (50 classes, 500 mages per class), without using
ADA in the training.
As the experiments show, the same observation happens in this setup in the absence of ADA, and our method is able to stabilize the training.
[Table]
A. E XPERIMENTS ON CIFAR
In addition to the experiments on the four datasets presented in the paper, we provide the results of training unconditional and conditional StyleGAN2+ADA as well as our method on four different subsets of CIFAR100 (Krizhevsky (2009)) in Table 6.
In Fig.
6, as an example, the FID curves of training the three methods on a subset of CIFAR100 with 100 classes and 300 images per class is visualized.
We observe a similar behavior on CIFAR100, where our approach outperforms the conditional baseline, achieving FID and KID better or on-par with the unconditional baseline. .
[Table]
[Figure]
Here, we provide the class-wise FID and KID for ImageNet Carnivores and Food101 datasets, using the full number of sample per class in calculating the metrics.
[Table]
A. P RECISION AND R ECALL
{+In tables 
+}
and
[Table]
we provide the unconditional and class-wise precision and recall metrics calculated for the compared methods
A. T HE T RANSITION F UNCTION
In Fig.
7, we provide a detailed visualization of the proposed transition function.
[Table]
[Table]
A BLATION ON THE TRANSITION IN THE LOSS FUNCTION
In table [Table] we provide the ablation over two choices for transition in the proposed loss function. A. V ISUAL R ESULTS
We provide more visual results for comparison of our method and the baselines in Fig. 8. Moreover, more generated images using our method on the four datasets are visualized in Figs. [Figure] [Equation]
[Figure]
[Equation]
[Figure]
Loss Formulation
[Table]
L = (1 − λ t ) ·
L uc + λ t · L c
[Table]
[Table]
[Table]
[Table]
[Table]
[Table]
L =
L uc + λ t · L c (Ours)
[Table]
UC - S t y l e G
AN
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AN +
L ec a m
C - S t y l e G
AN + P r o j D
B i g G
AN
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AN
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