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Found 3 papers, 3 papers with code
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images.
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders
In this paper, we propose a simple and end-to-end trainable deterministic autoencoding framework, that efficiently shapes the latent space of the model during training and utilizes the capacity of expressive multi-modal latent distributions.
Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation
Image generation has rapidly evolved in recent years.
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