A new extensible random module along with four selectable random numbergenerators and improved seeding designed for use in parallelprocesses has been added. The currently available bit generators are MT19937, PCG64, Philox, and SFC64. See below underNew Features.
Due to bugs in the application of log to random floating point numbers,the stream may change when sampling from beta, binomial,laplace, logistic, logseries ormultinomial if a 0 is generated in the underlying MT19937random stream. There is a 1 in\(10^{53}\) chance of this occurring, so the probability that the streamchanges for any given seed is extremely small. If a 0 is encountered in theunderlying generator, then the incorrect value produced (either numpy.inf ornumpy.nan) is now dropped.
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A new extensible numpy.random module along with four selectable random numbergenerators and improved seeding designed for use in parallel processes has beenadded. The currently available Bit Generators areMT19937, PCG64, Philox, and SFC64.PCG64 is the new default while MT19937 is retained for backwardscompatibility. Note that the legacy random module is unchanged and is nowfrozen, your current results will not change. More information is available inthe API change description and in the top-level view documentation.
This means that registering clip functions for custom dtypes in C viadescr->f->fastclip is deprecated - they should use the ufunc registrationmechanism instead, attaching to the np.core.umath.clip ufunc.
The goal of generative models, in general, is to generate new data points that conform to the distribution of the training dataset. To accomplish this goal, GANs consists of two networks, one named the generator that gets a random noise vector as input and outputs images. The second network is the discriminator that differentiates between real training images and fake ones created by the generator. In other words, the discriminator D classifies real images x as D(x) = 1 and the fake ones as D(x) = 0. The networks are trained in an adversarial manner to reach the Nash equilibrium, which is an optimal state where D(x) = for each image x, which means that the discriminator is not able to differentiate between real and fake samples [1].
One reason behind the success of GANs frameworks is that they have overcome some of the limitations of other generative models such as Variational Autoencoders (VAEs) [2]. For example, GANs frameworks produce sharper images compared to VAEs. The reconstruction loss function of VAEs is a pixel-wise similarity metric, while that of GANs is a semantic loss function [3, 4]. The issue with element-wise measures is that they do not align with the human visual system in the image domain; in other words, there are images that reflect a high element-wise error, however humans cannot distinguish between these images, and vice versa. The adversarial training in GANs facilitates building the reconstruction loss of the generator implicitly through the back-propagating gradients of the discriminator model that is responsible for differentiating between real images and fake ones.
In vanilla GANs [1], the model is unable to control the type of the generated samples. The generated data points could be from any category of the training data distribution. When sampling occurs, the generated samples might not represent all possible variations of the training data. In contrast, conditional GAN (CGAN) [13] adds a condition to both the generator and the discriminator, as shown in Figure 2(b). The condition might be a class, text or any other type of data, and the generated data is expected to match the condition. The loss function for CGAN is a modified version of the GANs loss function in Equation (1):
Information Maximizing GAN, InfoGAN [14] for short, uses a slightly different approach to control the generation process. As illustrated in Figure 2(c), the input in InfoGAN [14] to the generator is a noise vector along with another variable. Unlike the condition in CGAN, the variable vector in InfoGAN is unknown. The purpose of using the variable is to control specific properties in the generated samples by maximizing the mutual information between this variable and the generated samples. Through training InfoGAN, the generator learns how to disentangle certain properties in the generated samples in an unsupervised manner through another model called the auxiliary model. The auxiliary model shares the same parameters as the discriminator. The aim of the auxiliary model is to predict the properties that are disentangled while the discriminator's purpose is to distinguish real samples from fake ones. After training, the generated samples can be controlled by specifying some features that are learned such as colour, shape, rotation in image samples or class [14]. The loss function for InfoGAN is defined as follows:
Auxiliary Classifier GAN (AC-GAN) [15] is another framework under conditional GANs-based architectures. AC-GAN frameworks share characteristic with InfoGAN and another with CGAN. In terms of the similarity with CGAN, a condition is fed into the generator, which can be a text, class or any other type of data. However, the condition is not an input for the discriminator. The common factor between AC-GANs and InfoGAN is that there is an auxiliary classifier that outputs the class of the input sample. The differences in the overall architecture between CGAN, InfoGAN, and AC-GAN are demonstrated in Figure 2(b), 2(c), and 2(d), respectively. The loss function of AC-GAN is divided into two terms: one for evaluating the predicted class and another one to discriminate fake from real samples.
Vanilla GANs [1], considered as the simplest type of GANs, uses the Multi-Layer Perceptron (MLP) in both the generator and the discriminator. However, one of the main disadvantages of vanilla GANs is that the training process is not stable [12]. One of the possible solutions to this problem is to use a convolutional neural network (CNN) instead. In convolutional GANs [11], the generator uses a deconvolution structure, while the discriminator applies convolutional layers to classify generated images from the real images. While the type of networks in the generator and discriminator are different in vanilla GANs and convolutional GANs, the overall architecture in vanilla GANs and convolutional GANs are identical (see Figure 2(a)).
A significant number of recent GANs frameworks adopt CNN in their generators, discriminators, or in both. This is because the CNN in Convolutional GANs outperforms MLP in vanilla GANs in terms of the performance, quality of the resulted samples (usually images), and stability of the training process.
The GANs frameworks discussed so far, when applied in the image domain, produce low resolution images as output, i.e 64 64 or 128 128 images. In contrast, BigGAN [16], ProGAN [5], StyleGAN v1 [17] and StyleGAN v2 [18] scale up the output resolution to 256 256 and 512 512 using different methods. BigGAN [16] produces high-resolution images by increasing the number of parameters and scaling up the batch size. The architecture of BigGAN is based on the self-attention GAN (SAGAN) [19]. The main advantage of SAGAN is that it focuses on different parts of the image by introducing an attention map that is applied to the feature maps in the deep convolutional model architecture. ProGAN [5] implements a different technique by progressively adding layers to the network to scale up the generated images. The training procedure starts with 4 4 images and shallow convolutional layers until convergence, then builds up more layers to generate 8 8 images and so on until reaching the desired resolution. StyleGAN v1 [17] adopts a progressive architecture for the generator. The visual features can be controlled by lower layers, i.e 4 4 while finer styles are managed by 1024 resolution layers. StyleGAN v1 controls the generated features by mapping a random noise vector to an intermediate space using fully connected layers of the mapping network. Then, the intermediate space is used to control the features in the generator by Adaptive Instance Normalization (AdaIN) layers. AdaIN is an instance normalization technique that adds the intermediate style space to the content of the images. StyleGAN v2 [18] overcomes the artefacts that are generated by styleGAN v1 by removing AdaIN that has proven to be the main issue.
Since the original GANs frameworks were initially built upon images, there is no doubt that the number of GANs applications in the image domain surpasses other areas such as text, voice, and video. Multiple reviews [30, 31, 32, 33, 34] focus on image synthesis, even though the major proportion of the general GANs reviews mentioned above, are also in the image domain. Huang et al. [31] categorize the image synthesis GANs frameworks into three types based on the overall architecture. These include direct architectures based on vanilla GANs, hierarchal models and iterative models consisting of multiple generators and discriminators. While each generator in a hierarchical architecture is tasked to deal with a different aspect of the disentangled representations of training images, the goal of a generator in the iterative models is to refine the quality of the generated images. Wu et al. [32] place image GANs models in two main categories, namely conditional image synthesis models and unconditional image synthesis models. In the unconditional models, different network modules handle texture, image super resolution, image inpainting, face synthesis, and human synthesis. A comparative study [33] of image GANs frameworks was conducted using two datasets: MNIST [29] and Fashion-MNIST [35]. The paper reviews different applications of GANs such as style transfer, image inpainting, super-resolution, and text to image. Similar applications are also reviewed elsewhere [30], with additional applications such as face ageing and 3D image synthesis. Moreover, Agnese et al. [34] direct attention to GANs models that are conditioned on text and produce images. Some of these models fall under semantic enhancement GANs, where the main goal is to ensure that text is semantically coherent with the generated image. Another category focusses on producing high resolution images conditioned on text. An additional category is for ensuring the diversity of synthesized images based on input text. The last type is text to video models that consider the temporal dimension of the training samples. be457b7860
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