ai生成图片是什么技术_什么是生成型AI？

ai生成图片是什么技术

Generative AI opens the door to an entire world of creative possibilities with practical applications emerging across industries, from turning sketches into images for accelerated product development, to improving computer-aided design of complex objects.

生成式AI为跨行业出现的实际应用打开了具有创造力的整个世界的大门，从将草图转换为图像以加快产品开发速度，到改善复杂对象的计算机辅助设计。

For example, Glidewell Dental is training a generative adversarial network adept at constructing detailed 3D models from images. One network generates images and the second inspects those images. This results in an image that has even more anatomical detail than the original teeth they are replacing.

例如，Glidewell Dental正在训练一个生成对抗网络，该网络善于根据图像构造详细的3D模型。 一个网络生成图像，第二个网络检查这些图像。 这样生成的图像比其要替换的原始牙齿具有更多的解剖细节。

Glidewell Dental is training GPU powered GANs to create dental crown models Glidewell Dental正在训练GPU驱动的GAN以创建牙冠模型

Generative AI enables computers to learn the underlying pattern associated with a provided input (image, music, or text), and then they can use that input to generate new content. Examples of Generative AI techniques include Generative Adversarial Networks (GANs), Variational Autoencoders, and Transformers.

生成式AI使计算机能够学习与提供的输入(图像，音乐或文本)关联的底层模式，然后他们可以使用该输入来生成新内容。 生成AI技术的示例包括生成对抗网络(GAN)，变体自动编码器和变压器。

什么是GAN？ (What are GANs?)

GANs, a generative AI technique, pit 2 networks against each other to generate new content. The algorithm consists of two competing networks: a generator and a discriminator.

GAN是一种生成型AI技术，它使2个网络相互竞争以生成新内容。 该算法由两个相互竞争的网络组成： 生成器 和鉴别器 。

A generator is a convolutional neural network (CNN) that learns to create new data resembling the source data it was trained on.

生成器 是一个卷积神经网络(CNN)，它学习创建类似于训练数据源的新数据。

The discriminator is another convolutional neural network (CNN) that is trained to differentiate between real and synthetic data.

鉴别器 是另一个卷积神经网络(CNN)，经过训练可以区分真实数据和合成数据。

The generator and the discriminator are trained in alternating cycles such that the generator learns to produce more and more realistic data while the discriminator iteratively gets better at learning to differentiate real data from the newly created data.

生成器和鉴别器以交替的周期进行训练，以使生成器学习生成越来越多的真实数据，而鉴别器在学习将实际数据与新创建的数据区分开的过程中迭代地变得更好。

A schema representing a AWS DeepComposer GAN 表示AWS DeepComposer GAN的架构

Imagine a orchestra and conductor. An orchestra doesn’t create amazing music the first time they get together. They have a conductor who both judges their output, and coaches them to improve. So an orchestra, trains, practices, and tries to generate polished music, and then the conductor works with them, as both judge and coach.

想象一下乐队和指挥。 乐团第一次聚会不会创造出令人赞叹的音乐。 他们有一名指挥，他们既会判断自己的表现，又会指导他们进行改进。 因此，乐团训练，练习并尝试产生优美的音乐，然后指挥与他们一起担任法官和教练。

The conductor is both judging the quality of the output (were the right notes played with the right tempo) and at the same time providing feedback and coaching to the orchestra (“strings, more volume! Horns, softer in this part! Everyone, with feeling!”). Specifically to achieve a style that the conductor knows about. So, the more they work together the better the orchestra can perform.

指挥既要判断输出的质量(以正确的节奏演奏正确的音符)，又要向乐团提供反馈和指导(“弦，更大的音量！喇叭，这部分更柔和！每个人，感觉！”)。 特别是要获得指挥家了解的样式。 因此，他们在一起工作的次数越多，乐队的表现就越好。

The Generative AI that AWS DeepComposer teaches developers about uses a similar concept. We have two machine learning models that work together in order to learn how to generate musical compositions in distinctive styles.

AWS DeepComposer教给开发人员的创世式AI使用了类似的概念。 我们有两个可以协同工作的机器学习模型，以学习如何生成独特风格的音乐作品。

As a conductor provides feedback to make an orchestra sound better, a GAN’s discriminator gives the generator feedback on how to make its data more realistic 当指挥提供反馈以使管弦乐队的音质更好时，GAN的鉴别器会向生成器提供有关如何使数据更真实的反馈

今天如何使用生成型AI？ (How is generative AI being used today?)

As part of an ongoing effort, Airbus is re-imagining multiple structural aircraft components, applying Autodesk generative design to develop lighter weight parts that exceeds performance and safety standards. At 35 million miles, the trip to Mars is short compared to the 365 million miles journey to Jupiter, and then Saturn is another 381 million miles past that. Getting radars to these distant areas present far greater design and engineering challenges. To meet those challenges, JPL and Autodesk have engaged in a multi-year collaborative research project, so that JPL can explore new approaches to design and manufacturing processes for space exploration, when the custom application of Autodesk generative design technology.

作为正在进行的工作的一部分，空中客车公司正在重新构想飞机的多个结构部件，应用Autodesk的生成设计来开发重量轻的零件，这些零件的重量超过了性能和安全标准。 与前往木星的3.65亿英里相比，到达火星的行程短于3500万英里，而土星则比距木星的距离多了3.81亿英里。 将雷达带到这些遥远的地区提出了更大的设计和工程挑战。 为了应对这些挑战，JPL和Autodesk开展了一项多年合作研究项目，以便JPL可以在自定义应用Autodesk生成设计技术时探索用于太空探索的设计和制造流程的新方法。

Do you think GANs could be the next big thing?

您是否认为GAN可能是下一件大事？

翻译自: https://medium.com/@vinaykumarpaspula/what-is-generative-ai-89f9f9c0abe

ai生成图片是什么技术