175 citations · 201 across the 6 of their papers we have counts for
6 papers
Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Daniel Busch, Christian Bohn, Thomas Kurbiel +3
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We addres…
Improved Single Camera BEV Perception Using Multi-Camera Training
Daniel Busch, Ido Freeman, Richard Meyes +1
Bird's Eye View (BEV) map prediction is essential for downstream autonomous driving tasks like trajectory prediction. In the past, this was accomplished through the use of a sophis…
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
Richard Meyes, Constantin Waubert de Puiseau, Andres Posada-Moreno +1
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically chall…
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
Richard Meyes, Moritz Schneider, Tobias Meisen
The demand for more transparency of decision-making processes of deep reinforcement learning agents is greater than ever, due to their increased use in safety critical and ethicall…
Ablation Studies in Artificial Neural Networks
Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau +1
Ablation studies have been widely used in the field of neuroscience to tackle complex biological systems such as the extensively studied Drosophila central nervous system, the vert…
Ablation of a Robot's Brain: Neural Networks Under a Knife
Peter E. Lillian, Richard Meyes, Tobias Meisen
It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward. We present a novel method for determ…