AN OVERVIEW OF IMAGE GENERATION (SYNTHESIS)
Creating an image from diverse types of information such as text, graphs, and object layout is a difficult task in computer vision. Moreover, capturing images from various perspectives to generate an object or product manually can be tedious and expensive. However, with the advent of deep learning and artificial intelligence methods, it has become feasible to generate new images from different types of data. Consequently, there has been a considerable endeavor to develop image-generation strategies, which has yielded significant progress.
What Is Image Generation?
Image generation is the process of creating new images/pictures using a set of pre-existing data.
Synthetic Image Generation
Synthetic Image generation refers to the creation of images that look realistic and have been generated artificially. One method of achieving this is through the use of Generation Adversarial Networks (GANs) which involves a generator-discriminator architecture to create and evaluate synthetic images. The process is repeated multiple times until the generated image is realistic enough to fool the discriminator. Another method is through Variational Autoencoders, including Vector Quantized Variational Autoencoders (VQ-VAE), which use a discrete latent representation and provide a wider range of images, and are easier to train than GANs.
It is now possible to generate images by providing a short description of the image. This is thanks to the advancements in GANs and Variational Autoencoders. In this article, we will explore various models and architectures that can make this happen.
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Impact Of Synthetic Image Generation
The creation of artificial pictures can have different effects depending on the way it’s applied. It can be used to address various concerns in machine learning enterprises and also assist in resolving the lack of genuine information.
Advantages Of Synthetic Image Generation
- Creating artificial image data can help solve the problem of not having enough real images for businesses.
- Conversational chatbots can use technology to generate images that are related to the user’s conversation.
- When there is not enough variety in real image data, synthetic images can be used to help train machine learning models. By adding more types of images to the dataset, the model can learn better.
- Search engines can use text-to-image generation to make images quickly and without infringing on copyrights. This can also be helpful when there are not many real images available.
Disadvantages Of Synthetic Image Generation
- Artificial image creation can be misused to produce misleading images that can trick people.
- Generating synthetic images with poor accuracy and realism can worsen the quality of the current image dataset rather than improve it.
Text To Image Generation Models
There are different ways to teach computers to create artificial pictures from text data. In this article, we will examine a few of these methods closely.
- DALL-E
OpenAI created a neural network called DALL-E, which has been trained using 12 billion parameters on image-text pairs. This network can create synthetic images based on any given text description. DALL-E can produce various types of images, including anthropomorphic animals and objects, and can add diversity and transform images. It can even combine unrelated aspects and details of images believably.
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DALL-E has generated many images as examples, including a snail made of harps, an avocado chair, a polar bear in a jungle, an illustration of a baby daikon radish in a tutu walking a dog, and many more.
The structure of DALL-E is created using Vector Quantized Variational Autoencoder (VQ-VAE) that produces a distinct coded representation. The system includes an encoder-decoder format, and compared to traditional autoencoders, VQ-VAE has an added codebook component that contains a group of vectors associated with an index number that helps encode the bottleneck of the autoencoder. The encoded output of the encoder network is matched with the vectors in the codebook, and the vector in the codebook that has the nearest Euclidean distance is sent to the decoder.
To comprehend how DALL-E functions and test a visual interface where you can input keywords and produce synthetic images, you can experiment with DALL-E mini since OpenAI hasn’t published the full details of DALL-E.
- Text to Image with CLIP
The CLIP is a computer program that can understand pictures and words together. It can be trained to guess what words would best describe a picture. It learns how a sentence and an image are related, so when it sees a sentence, it can find the most accurate picture to go with it.
CLIP Architecture is a kind of computer program that can guess what an image is, even if it has never seen that kind of image before. It is like teaching a program to recognize only cats and dogs, but then it can still recognize rabbits because it knows what animals look like. CLIP is especially good at this kind of guessing because it looks at both pictures and words together.
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Here are some more examples:
- StackGAN is a type of technology that uses Stacked Generative Adversarial Networks to create realistic-looking images from text.
- AttnGAN is another type of technology that uses Attentional Generative Adversarial Networks to generate images from text. It can create very detailed images by focusing on specific words in the text.
- SSA-GAN is a technology that uses Semantic Spatial Aware Generative Adversarial Networks to produce images that match the meaning of the text.
Conclusion
Generating images using text-based techniques can have multiple practical applications, such as training machine learning models that lack adequate image data, creating contextual images for chatbots, and providing search engines and stock photos with more options. Due to the recent advancements in neural networks, this technology can be readily accessed and utilized, and future enhancements could lead to synthetic images replacing the need for real images in scenarios where they are scarce or costly.
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