| Lab courses | An Introduction to Python for Data Analysis |
| Lab courses | An Introduction to Python for Data Analysis |
| Lab courses | An Introduction to Python for Data Analysis |
Can a single-level VQ-VAE, with matched representational budget and no codebook collapse, equal the reconstruction fidelity of its hierarchical counterpart? We revisit this question by comparing a two-level hierarchical VQ-VAE and a capacity-matched single-level model on high-resolution ImageNet images.
In this study, we revisit feature inversion, introducing a novel, modular variation that enables significantly more efficient application of the technique. We demonstrate how our method can be systematically applied to the large-scale transformer-based vision models, Detection Transformer and Vision Transformer, and how reconstructed images can be qualitatively interpreted in a meaningful way.
In this study we develop and analyze hierarchical computational models that complements episodic memory with semantic information, looking into how attention affects the recall process.
The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
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