38 citations · 57 across the 8 of their papers we have counts for
3 papers · 1 filter
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning
Timm Hess, Martin Mundt, Iuliia Pliushch +1
Several families of continual learning techniques have been proposed to alleviate catastrophic interference in deep neural network training on non-stationary data. However, a compr…
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics
Iuliia Pliushch, Martin Mundt, Nicolas Lupp +1
Although a plethora of architectural variants for deep classification has been introduced over time, recent works have found empirical evidence towards similarities in their traini…
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference
Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh
In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstr…