The exponential growth of global data has intensified the demand for efficient data compression, with deep learning techniques like variational autoencoders, generative adversarial networks (GANs), diffusion models, and implicit neural representations reshaping traditional approaches to source coding. Learning-based neural compression methods have demonstrated the potential to outperform traditional codecs across various data modalities like image, video, and audio. However, challenges remain in improving their computational efficiency and memory requirements, understanding the theoretical limits of neural compression and compression without quantization as well as addressing challenges in distributed settings.

In parallel, compression has emerged as a powerful proxy task for advancing broader learning objectives, including representation learning and model efficiency. Recent research is exploring how compression can enhance training and generalization of large-scale foundation models for vision, language, and multi-modal applications. Techniques like knowledge distillation, model pruning, and quantization share common challenges with compression, highlighting the symbiotic relationship between these seemingly distant concepts. The intersection of learning, compression and information theory offers exciting new avenues for advancing both practical compression techniques and also our understanding of deep learning dynamics.

This workshop aims to unite experts from machine learning, computer science, and information theory to delve into the dual themes of learning-based compression and using compression as a tool for learning tasks. We are excited to feature industry experts who will share valuable practical insights with the ISIT attendees.

If you are interested in submitting a paper, please refer to the Call for Papers for more information.

Papers can be submitted via this EDAS link. Note: The EDAS submission form is currently titled "Register a paper for 2025 IEEE International Symposium on Information Theory (ISIT)", without reference to the workshop. Please disregard this; you can safely register your paper(s) through the form, they will be correctly assigned to the workshop.

If you are interested in reviewing, please drop us a quick email at learn.to.compress.workshop@gmail.com, or fill out this form directly.

The workshop will be held (in-person) on Thursday 26 June 2025 - we look forward to seeing you in Ann Arbor!

Important Dates

Paper submission deadline: ~~March 14~~ March 28, 2025 (11:59 PM, anywhere in the world!).
Decision notification: April 18, 2025
Camera-ready paper deadline: May 1, 2025
Workshop date: June 26, 2025