For what purpose was the dataset created? To foster the research in aligning diffusion models further and analyze the user preferences. Who created the dataset (e.g., which team, research group) and on behalf of which entity Who funded the creation of the dataset? What do the instances that comprise the dataset represent (e.g., documents, photos, people, How many instances are there in total (of each type, if appropriate)? What data does each instance consist of?

Real images encode a lot of information about the world, such as how an object can look like, how certain things can be meaningfully arranged, or which items belong together. The image of an average office desk can give us information about how the different parts are usually arranged in relation to each other, e.g. a monitor on the desk with mouse and keyboard in front of it and a chair in front of the desk, or the image of someone preparing a meal can give us information about which ingredients and kitchen tools are to be used. This might seem rather trivial from a human perspective as we are very easily capable of handling such tasks without having to rely on pre-made example images to follow, but for a robot that has to navigate and solve tasks in e.g. a household environment such information can be critical for successfully handling everyday-activities and interacting with the world. We could encode all relevant information explicitly into an extensive knowledge base [1] for the robot, but considering the number of tasks and circumstances that a robot could encounter, correctly handling all situations could become very challenging [2] or even overwhelming when the robot needs to act in widely different environments. Additional knowledge sources, such as simulations of the environment, when available, can help by providing ways to investigate consequences of actions without having to act in the world [3]. We could also try to train the robot on a variety of different tasks, e.g. using reinforcement learning or other methods [4], hoping that the robot is able to generalize and handle situations and circumstances that were never seen during training. However, images of the real world already show examples of how a dining table looks like with plates and cutlery, how images are hung on the wall in bedrooms, dining rooms, or other places. Figure 1 shows an example of two different versions of how sandwich ingredients could be stacked together.

Yuki Mitsufuji is a Lead Research Scientist at Sony AI. Yuki and his team presented two papers at the recent Conference on Neural Information Processing Systems (NeurIPS 2024). These works tackle different aspects of image generation and are entitled: GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping and PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher . We caught up with Yuki to find out more about this research. The problem we aimed to solve is called single-shot novel view synthesis, which is where you have one image and want to create another image of the same scene from a different camera angle. There has been a lot of work in this space, but a major challenge remains: when an image angle changes substantially, the image quality degrades significantly.

Image generation by AI is based on "generative models," a type of deep learning technology. Generative models can learn patterns in given data and generate new data similar to that data. There are two types of generative models: discriminative models, which solve problems such as classification and regression through supervised learning, and generative models, which generate new data. Discriminative models extract features from data to perform classifications, etc., while generative models can generate data from random noise. A generative model called a Generative Adversarial Network (GAN) was proposed by Ian Goodfellow in 2014; a GAN can generate realistic images by pitting two neural networks against each other.

Microsoft's Bing AI chat can already be helpful for finding answers, but now it can help you produce fanciful pictures. The company has introduced a Bing Image Creator preview that adds OpenAI's DALL-E AI image generation to both Bing search and a sidebar in the Edge browser. You just have to ask the chatbot to create an image with either a direct description or a follow-up to a previous query. If you're wondering how to revamp your living room, you can ask Bing to draw some ideas based on your criteria. Yes, Microsoft is aware of the potential for things to go awry.

Summary: Text-to-image generation deep learning models like OpenAI's DALL-E 2 can be a promising new tool for image augmentation, generation, and manipulation in a healthcare setting. A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care. Do generative models have sufficient medical domain knowledge to provide accurate and useful results? Dr Lisa C Adams and colleagues explore this topic in their latest viewpoint titled "What Does DALL-E 2 Know About Radiology?" First introduced by OpenAI in April 2022, DALL-E 2 is an artificial intelligence (AI) tool that has gained popularity for generating novel photorealistic images or artwork based on textual input.

A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care. Do generative models have sufficient medical domain knowledge to provide accurate and useful results? Dr. Lisa C Adams and colleagues explore this topic in their latest viewpoint titled "What Does DALL-E 2 Know About Radiology?" First introduced by OpenAI in April 2022, DALL-E 2 is an artificial intelligence (AI) tool that has gained popularity for generating novel photorealistic images or artwork based on textual input. DALL-E 2's generative capabilities are powerful, as it has been trained on billions of existing text-image pairs off the internet.

In current models, the only way to describe where an object should be placed in an AI image is with text – with only moderate success. Researchers now present a model that uses bounding boxes. AI image generation has rapidly evolved from diffuse visualizations to very concrete, sometimes even photorealistic results. The more detailed the specification, the better the generation can be influenced. Although details of the image composition can be described with text, such as where an object should be placed, these details are often only moderately implemented.

'Woman reading book, under a night sky, dreamy atmosphere," I type into Deep Dream Generator's Text 2 Dream feature. In less than a minute, an image is returned to me showing what I've described. Welcome to the world of AI image generation, where you can create what on the surface looks like top-notch artwork using just a few text prompts, even if in reality your skills don't go beyond drawing stick figures. AI image generation seems to be everywhere: on TikTok, the popular AI Manga filter shows you what you look like in the Japanese comic style, while people in their droves are using it to create images for everything from company logos to picture books. It's already been used by one major publisher: sci-fi imprint Tor discovered that a cover it had created had used a licensed image created by AI, but decided to go ahead anyway "due to production constraints". The biggest players in AI include companies such as MidJourney, Stable Diffusion and Deep Dream Generator (DDG). They're free to use, up to a point, making them attractive to those just wanting to try them out. There's no denying that they're fun, but closer examination of the images they produce shows oddities. The face of the woman in my image has very odd features, and appears to be holding multiple books. The images also have a similarly polished, somewhat kitsch aesthetic. And, while there's an initial thrill at seeing an image appear, there's no creative satisfaction. The implications of AI image generation are far-reaching and could impact everything from film to graphic novels and more. Children's illustrators were quick to raise concerns about the technology on social media. Among them is author and illustrator Rob Biddulph, who says that AI-generated art "is the exact opposite of what I believe art to be.