Forgetting is not the opposite of learning but its responsible counterpart.
Denver, Colorado • June 2026
Machine unlearning has emerged as a crucial capability for computer vision models that must forget, remove, or steer away from undesired data or concepts. This workshop (in short, MUV) brings together researchers working on unlearning methods for recognition and generative tasks, including selective forgetting, safe image and video generation, privacy-preserving learning, and ethical compliance under regulations such as GDPR and CCPA.
By uniting technical advances with responsible AI practices, MUV aims to establish unlearning as a cornerstone of trustworthy visual intelligence. Forgetting is not the opposite of learning but its responsible counterpart.
Google DeepMind
Northeastern University
Michigan State University
MIT
Google DeepMind
IIT Jodhpur
MUV’s objective is to facilitate engaging and influential discussions across the following topics:
Full papers (up to 8 pages). Accepted papers will be included in CVPR proceedings.
Extended abstracts (up to 4 pages) or already published works (up to 8 pages). Ideal for preliminary results or stimulating discussion.
| Time | Event |
|---|---|
| 08:30 - 08:40 | Opening Remarks |
| 08:40 - 09:10 | Invited Talk: Antonio Torralba |
| 09:10 - 09:40 | Invited Talk: Rohit Gandikota |
| 09:40 - 10:10 | Invited Talk: Sijia Liu |
| 10:10 - 10:40 | Coffee Break |
| 10:40 - 11:10 | Invited Talk: Gintare Karolina Dziugaite |
| 11:10 - 11:40 | Invited Talk: Eleni Triantafillou |
| 11:40 - 12:10 | Invited Talk: Mayank Vatsa |
| 12:10 - 12:50 | Poster Session & Oral Spotlights |
| 12:50 - 13:00 | Closing Remarks |
Sapienza University of Rome
Sapienza University of Rome
Meta
TU Munich
ItalAI
MPI for Informatics