Examples of using graphical neural networks for four industries: marketplaces, fashion design, interior design, and event organization.

Generative neural networks have been a sensational technological trend for the last two or three years. Can it bring concrete benefits to business? Let’s tell you about it with clear examples.

What generative neural networks are and how they differ from conventional neural networks

Unlike a conventional neural network, the generative AI family is built using basic models and has capabilities that earlier AI did not have, such as the ability to generate content. This includes creating new images, videos, and music on their own.

Generative AI models are trained using deep learning from large datasets and patterns. They can then generate new examples similar to the training data. And for this, they don’t need a labeled dataset, where each image specifies which image element is called what. A generative neural network can recognize such elements by itself and generate similar ones.

Popular examples of generative models are GPT-4, Midjourney, PaLM, DALL-E 2, and Stable Diffusion.

Examples of using generative neural networks in business

One of the application possibilities is to automate typical and routine business tasks of content creation that take a lot of time. This is why Generative AI Design Services from various companies are in such demand today.

Marketplaces and online stores

Generative neural networks can speed up work on marketplaces many times over. For example, to replace the background of a subject shot, retouch a photo, or create a description for a product card.

• Generating a description for a product card

The most popular way is to request ChatGPT. To do this, clearly state the task and describe the order in which it needs to be accomplished. Give context. Explain to ChatGPT who your target audience is and what goal you want to achieve. The more context you give, the more relevant, personalized, and useful the response will be. Specify the writing style. Write in what form the response should be presented.

• Generate a visual series for a product card

DALL-E, Midjourney, and Stable Diffusion – neural networks that generate images based on text descriptions or input images – are suitable for this.

Fashion design and virtual fitting

Trained generative neural networks in “company” with other models can enrich ready-made clothes with new design ideas or generate more realistic models (for example, for virtual fitting of clothes).

First, you need a photo of a model, preferably full-length and on a neutral background. Then use the tools of the selected neural network to create a “paint mask”. And apply various prompts to it. For example, you can describe elements of clothing. To do this, describe in detail in the query what the model should be wearing.

You can also add the type of clothing (e.g. Culotte dress, Babydoll dress, Wrap around dress, Kimono dress). To add creativity, add emotional words (e.g. elegant, stylish, stunning). Directly describing new design ideas is an effective method, but not the only one.

Interior and interior design

In the field of design, a generative neural network helps automate initial customer inquiries: for example, generating generic images.

Such solutions are suitable for furniture manufacturing companies to quickly “try on” their samples at customer sites. For interior designers and design bureaus to speed up the approval and development of prototypes. For real estate developers who want to offer clients to generate generic design projects.

Digital design

In the field of advertising, generative neural networks are capable of creating scripts for commercials and coming up with ideas for promotions. Any text-based generative neural network can behave like a marketer if it is given such a condition find ideas for a business name, and develop promotions for customers or strategy based on uploaded data.

In the event industry, neural networks handle typical design tasks, such as designing standard exhibition stands in different styles.

Conclusion

Implementing neural networks allows you to create unique solutions several times faster. Time is the currency of the new age, and in business today, those who are the first to innovate and the fastest to deliver quality services and products are the winners.

The use of generative neural networks, in addition to creating content and ideas for product design, helps to quickly find solutions, conduct research, and predict results. The analytical capabilities of neural networks save enterprise resources help make better decisions and improve business processes in any business. New digital systems and services based on neural networks, as well as the companies that utilize them, significantly benefit compared to others on the market.