77 citations · 127 across the 19 of their papers we have counts for
3 papers · 1 filter
On the Dangers of Bootstrapping Generation for Continual Learning and Beyond
Daniil Zverev, A. Sophia Koepke, Joao F. Henriques
The use of synthetically generated data for training models is becoming a common practice. While generated data can augment the training data, repeated training on synthetic data r…
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Karsten Roth, Lukas Thede, Almut Sophia Koepke +3
Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization. In this work, we find these training variati…
Addressing caveats of neural persistence with deep graph persistence
Leander Girrbach, Anders Christensen, Ole Winther +2
Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, how…