The article introduces SODA, a self-supervised diffusion model designed for representation learning. The model includes an image encoder that transforms a source view into a compact representation, which guides the…
Continue readingThe article ‘Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models’ addresses the problem of synthesizing multi-view optical illusions. These are images that change appearance upon a transformation, such as…
Continue readingThe paper explores the potential of diffusion models in unsupervised learning. Such models use a U-Net to predict and remove noise, resulting in the synthesis of high-quality, diverse images. The…
Continue readingThe article discusses the challenges of protecting image copyright in the context of diffusion models (DMs), which are advanced image synthesis methods. The authors propose a novel technique, Robust Invisible…
Continue readingThe article presents a new dataset, FIRST, designed for text-driven fashion synthesis and design. It’s part of the artificial intelligence generative content (AIGC) that can bring a significant revolution in…
Continue readingThis paper discusses the use of denoising diffusion probabilistic models (DDPM) for medical image segmentation. The researchers highlight the challenges in this field, such as limited dataset sizes and the…
Continue readingThe paper by Mark Levy and others from Apple demonstrates how conditional generation from diffusion models can be applied to various tasks in music production. This includes continuation, inpainting, and…
Continue readingThe article introduces a novel end-to-end text-to-speech model called E3 TTS, which is based on diffusion. Unlike previous models, E3 TTS does not rely on intermediate representations such as spectrogram…
Continue readingThe article presents a model for symbolic music generation that is controlled by emotions. This model combines diffusion models with Generative Adversarial Networks (GANs). While diffusion models have been successful…
Continue readingThe article discusses the use of Denoising Diffusion Probabilistic Models (DDPM) in the generation of symbolic music with desired composer styles. The authors propose the combination of a vector quantized…
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