In recent years, many crimes use technology to generate someone's face which has a bad effect on that person. Generative adversarial network is method fake images using discriminators and generators. Conventional GAN involved binary cross entropy loss for discriminator training classify original image from dataset generated generator. However, of cannot provided gradient information generator i...

Larger object classes often become more costly classes in the maintenance phase of object-oriented software. Consequently class would have to be constructed in a medium or small size. In order to discuss such desirable size, this paper proposes a simple method for predictively discriminating costly classes in version-upgrades, using a class size metric, Stmts. Concretely, a threshold value of c...

One popular generative model that has high quality results is the Generative Adversarial Networks(GAN). This type of architecture consists of two separate networks that play against each other. The generator creates an output from the input noise that is given to it. The discriminator has the task of determining if the input to it is real or fake. This takes place constantly eventually leads to...

Generative Adversarial Networks (GANs) were intuitively and attractively explained under the perspective of game theory, wherein two involving parties are a discriminator and a generator. In this game, the task of the discriminator is to discriminate the real and generated (i.e., fake) data, whilst the task of the generator is to generate the fake data that maximally confuses the discriminator....

Nuclear and High Energy Physics experiments often deal with a large number of detectors. These detectors give out analog signals depicting several parameters like time of arrival, amplitude and width of these analog signals, and pulse count rate etc. Usually, fast discriminators are used to convert these analog signals to digital form, crossing a set threshold. We have designed and developed 8 ...

Image semantic completion is to employ remaining image information restore the damaged or missing areas. Face task usually more challenging than other inpainting problems as it requires stronger consistency. We proposed a contextual feature constrained DCGAN with paired discriminator inpaint face images, which capable of overcoming DCGAN's shortages insufficient learning capability and unstable...

[lo] H. Cramer and M. Leadbetter, “The moments of the number of [13] L. Ehrman, “Analysis of a zero-crossing frequency discriminator crossings of a level by a stationary normal process,” Ann. Math. with random inputs,” IEEE Trans. Aerospace and Navigational Stat., vol. 36, pp. 165661663, 1965. Electronics, vol. ANE-12, pp. 113-119, June 1965. [II] M. Loeve, Probability Theory. New York: Van Nos...