Generative Neural Scene Representations for 3D-Aware Image Synthesis
Explore 3D-aware image synthesis using generative neural scene representations, covering GRAF, GIRAFFE, and CAMPARI models for advanced computer vision applications.
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Explores unsupervised learning for 3D-controllable image synthesis, disentangling latent factors like viewpoint and object pose from raw images to create novel, consistent scenes.
Convergence of Denoising Diffusion Models Under the Manifold Hypothesis
Explores theoretical analysis of denoising diffusion models, providing convergence results for data distributions on lower-dimensional manifolds or empirical distributions, with quantitative bounds on Wasserstein distance.
PixArt-α: Open Source Text-to-Image Model - Installation and Usage Tutorial
Explore PixArt-α, an open-source text-to-image model rivaling Midjourney. Learn installation, usage, and comparison with other models. Discover its superior capabilities in various art styles and prompt following.
What is Generative AI - The Turing Lectures
Explore generative AI's capabilities, applications, and controversies with Professor Mirella Lapata. Learn about text, image, speech, and music generation in this comprehensive overview of a rapidly evolving field.
The Turing Lectures - Addressing the Risks of Generative AI
Exploring ethical challenges and societal impacts of generative AI, focusing on potential misuse, regulatory gaps, and strategies for responsible development and implementation.
Synthetic Data in Medical Imaging: Are We There Yet?
Explore synthetic data's potential and challenges in medical imaging, focusing on recent advancements and future prospects for improved healthcare applications.
Understanding Neural Network Architectures with Attention and Diffusion
Explore Attention and Diffusion models in neural networks, focusing on their applications in language processing and image synthesis, with simplified diagrams for visual learners.
Story Generation Using Stable Diffusion in Python
Explore coherent visual story synthesis using AR-LDM, a latent diffusion model for story visualization and continuation tasks, leveraging history captions and generated images.
A Spline Tour of Deep Learning: The Scattering Way
Explore deep learning's progress, challenges, and theoretical foundations. Gain insights into the geometric interpretation of deep networks and spline approximation, with a focus on Scattering Networks.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in debiasing and image synthesis.
Vector Databases for Generative AI Applications
Explore vector databases' role in generative AI, covering concepts like RAG and efficient open-source tools for building AI applications.
The New Era of Image Denoising
Explore revolutionary image denoising techniques, their applications in inverse problems, and the concept of diverse solutions in image processing.
Elucidating the Design Space of Diffusion-Based Generative Models
Explore advancements in diffusion-based generative models, including improved sampling, training, and score network preconditioning. Learn about state-of-the-art results and modular design changes for enhanced efficiency and quality.
Neural Scene Representations for Learning-based View Synthesis and Its Applications
Explore neural scene representations for efficient image generation, novel view synthesis, and advanced applications in computer graphics and 3D vision.
Differentially Private Diffusion Probabilistic Models for Image Synthesis
Explore how dp-promise leverages diffusion model noise to achieve differential privacy in image synthesis while maintaining high image quality and protecting sensitive training data.