Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v37i13.26943Keywords:
Computer Vision, Incremental Learning, Concept Drift, Catastrophic InterferenceAbstract
This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.Downloads
Published
2023-09-06
How to Cite
Bourbeau, C., & Durand, A. (2023). Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16166-16167. https://doi.org/10.1609/aaai.v37i13.26943
Issue
Section
AAAI Student Abstract and Poster Program