Learning latent representations across multiple data domains using Lifelong VAEGAN
This addresses the problem of catastrophic forgetting for lifelong learning systems, offering an incremental improvement over existing generative replay methods by adding latent representation capabilities.
The paper tackles catastrophic forgetting in sequential deep learning by introducing Lifelong VAEGAN, which learns meaningful latent representations across multiple data domains and enables tasks like interpolation and inference, achieving competitive performance on benchmarks.
The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM), have been proposed to reproduce previously learned knowledge aiming to reduce the forgetting. However, such approaches lack an appropriate inference model and therefore can not provide latent representations of data. In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning. L-VAEGAN can allow to automatically embed the information associated with different domains into several clusters in the latent space, while also capturing semantically meaningful shared latent variables, across different data domains. The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.