all AI news
Generative Multi-modal Models are Good Class-Incremental Learners
March 28, 2024, 4:42 a.m. | Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu, Ming-Ming Cheng
cs.LG updates on arXiv.org arxiv.org
Abstract: In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models, we would explore replacing discriminative models with generative ones for CIL. However, transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated …
arxiv class cs.ai cs.cv cs.lg generative good incremental modal multi-modal type
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 11 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 11 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA