Neural network training method and device and image generation method and device
A neural network training and network technology, applied in the computer field, can solve problems such as unstable training, poor image quality, and information loss
Active Publication Date: 2021-07-20
BEIJING SENSETIME TECH DEV CO LTD
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However, the authenticity of the image is reflected in multiple dimensions, such as color, texture, scale, background, etc. Using a single scalar will cause information loss to a certain extent, and give the neural network biased guidance, resulting in unstable training and image quality. bad question
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[0117] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
[0118] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or better than other embodiments.
[0119]The term "and / or" in this article is just an association relationship describing associated objects, which means that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and there exists alone B these three situations. In addition, the term "at least one" herein means...
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The present disclosure relates to a neural network training method and device, and an image generation method and device. The method includes: inputting a first random vector into the generating network to obtain a first generated image; inputting the first generated image and the first real image into a discriminant network to obtain the first discriminant distribution and the second discriminant distribution; according to the first discriminant distribution, the second discriminant distribution, the first target distribution, and the second target distribution, determine the first network loss of the discriminant network; according to the first discriminant distribution and the second discriminant distribution The second discriminant distribution determines the second network loss of the generating network; according to the first network loss and the second network loss, confrontation training generates the network and the discriminant network. According to the neural network training method of the embodiments of the present disclosure, the discriminant network can output a discriminant distribution for the input image, describe the authenticity of the input image in the form of a probability distribution, and can consider the authenticity of the input image from multiple aspects to reduce information loss. Improve training accuracy.
Description
technical field [0001] The present disclosure relates to the field of computer technology, in particular to a neural network training method and device, and an image generation method and device. Background technique [0002] In related technologies, a Generative Adversarial Networks (GAN) consists of two modules, namely a discriminator network (Discriminator) and a generation network (Generator). Inspired by a zero-sum game, the two networks compete against each other to achieve the best generation effect. During the training process, the discriminator learns to distinguish between real image data and simulated images generated by the network by rewarding real targets and punishing false targets, and the generator gradually reduces the penalty of the discriminator for false targets, so that the discriminator cannot distinguish real images With the generated image, the two compete with each other and evolve, and finally achieve the effect of confusing the real. [0003] In...
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IPC IPC(8): G06T9/00G06N3/08G06V10/762G06V10/776
CPCG06T9/002G06N3/084G06N3/08G06V10/762G06V10/776G06V10/7747G06N3/047G06N3/045G06F18/23G06N7/01G06V10/82G06T7/0002G06T2207/20076G06T2207/20081G06T2207/20084G06F18/217G06F18/2148
Inventor 邓煜彬戴勃相里元博林达华吕健勤
Owner BEIJING SENSETIME TECH DEV CO LTD