80 citations · 179 across the 16 of their papers we have counts for
4 papers · 1 filter
An Investigation of Replay-based Approaches for Continual Learning
Benedikt Bagus, Alexander Gepperth
Continual learning (CL) is a major challenge of machine learning (ML) and describes the ability to learn several tasks sequentially without catastrophic forgetting (CF). Recent wor…
Image Modeling with Deep Convolutional Gaussian Mixture Models
Alexander Gepperth, Benedikt Pfülb
In this conceptual work, we present Deep Convolutional Gaussian Mixture Models (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models (GMMs) that is particularly…
Continual Learning with Fully Probabilistic Models
Benedikt Pfülb, Alexander Gepperth, Benedikt Bagus
We present an approach for continual learning (CL) that is based on fully probabilistic (or generative) models of machine learning. In contrast to, e.g., GANs that are "generative"…
Overcoming Catastrophic Forgetting with Gaussian Mixture Replay
Benedikt Pfülb, Alexander Gepperth
We present Gaussian Mixture Replay (GMR), a rehearsal-based approach for continual learning (CL) based on Gaussian Mixture Models (GMM). CL approaches are intended to tackle the pr…