Trending

Bookmarks

Keep Up With Latest Trending Papers. Computer Science, AI and Machine Learning and more.Subscribe

Machine Learning

Computer Vision

A Diffusion Probabilistic Model for Handwriting Generation

Diffusion models for Handwriting Generation

Diffusion probabilistic models are a class of generative models where samples start from gaussian noise and are gradually denoised to produce output.

In this paper, we propose a diffusion probabilistic model for handwriting generation.

Our model is able to incorporate writerstylistic features directly from image data, eliminating the need for userinteraction during sampling.

Experiments reveal that our model is able to generate realistic, high quality images of handwritten text in a similar style to a given writer.

Authors

Troy Luhman, Eric Luhman

Related Topics

Generative models

Diffusion models

Auxiliary networks

Diffusion probabilistic model

Read the Paper

Read Paper

Trending

Bookmarks

◐ Latest Activity

More

◐ Recommended Follows

Founder 42papers.com. Also bui...

Average CS student at ULL. I l...

Vojtěch Létal

More

A Diffusion Probabilistic Model for Handwriting Generation - 42Papers

Read Related Papers

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

A generative adversarial network for style augmentation for nuclei segmentation

A generative adversarial network based image transformation policy for visual self-supervision

A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains

A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context

Latent Space Direction for Generative Adversarial Networks

Stylized Adversarial Defense

Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks

Professor Forcing: A New Algorithm for Training Recurrent Networks

Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE