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Are there any courses about image synthesis or generative models?

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There are several courses available that cover image synthesis and generative models, which are key areas in the field of machine learning and AI. Here are some notable ones:

1. 16-726 Learning-Based Image Synthesis (Spring 2022 & Spring 2023): This course provides an in-depth exploration of image synthesis and generative models. It includes topics such as generative models, conditional GANs, and image-to-image translation. The course also covers various advanced techniques and models like StyleGAN, Latent Diffusion Models, and Denoising Diffusion Probabilistic Models. You can find detailed reading lists for each topic, which include influential research papers and foundational works in the field​​​​.
2. Deep Generative Models: Imitation Learning, Image Synthesis, and Compression: Offered by the EECS Department at the University of California, Berkeley, this course dives into deep generative modeling, addressing challenges in high-dimensional and high-entropy data. The course includes topics like imitation learning and the use of flow models for image synthesis and compression​​.
3. Diffusion and Score-Based Generative Models at MIT: As part of the Brain and Cognitive Sciences Computational Tutorial Series, this non-credit course at MIT covers diffusion and score-based generative models, offering insights into the latest developments in the field. It's an excellent resource for those interested in the intersection of neuroscience, cognitive science, and generative models​​.

These courses are suitable for individuals with a background in machine learning and computer science, as they delve into advanced topics and require a solid understanding of foundational concepts in these fields. The courses often incorporate recent research and developments, making them a valuable resource for staying updated in this rapidly evolving area.

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What are the latest research works in this domain?

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Recent advancements in image synthesis and generative models have led to some intriguing developments in 2024:

1. PFGM++ Model: A team at MIT developed a new generative AI model called PFGM++, which has shown to outperform traditional diffusion models in image generation. This model extends the electric field concept in PFGM to a more complex, higher-dimensional framework, finding a balance between robustness and ease of use. It demonstrates high resistance to errors and robustness towards the step size in differential equations. The PFGM++ model is significant for its efficiency in image and pattern generation and has potential applications in various fields, from digital content creation to generative drug discovery​​.
2. Hourglass Diffusion Transformer (HDiT): This model exhibits linear scaling with pixel count and supports high-resolution training directly in pixel-space, bridging the gap between the efficiency of convolutional U-Nets and the scalability of Transformers. HDiT has been successful without the need for typical high-resolution training techniques and has set a new state-of-the-art for diffusion models on the FFHQ-$1024^2$ dataset​​.
3. StableRep and StableRep+ Models: Developed by MIT researchers, these models use synthetic images generated by text-to-image models for training. They've shown that models trained with synthetic images can outperform those trained with real images in large-scale settings. StableRep+ notably achieved superior accuracy and efficiency compared to CLIP models trained with 50 million real images. However, this approach still requires initial training on large-scale real data​​.

These developments represent significant strides in the field of generative AI, especially in image synthesis, offering more efficient, scalable, and accurate models. They not only improve the quality and efficiency of image generation but also open up new avenues for applications in various domains.