Resource Consumption and Development Effort in GPU-Optimized Industrial Anomaly Detection Inference: A Comparison between PyTorch and a C++/CUDA Pipeline | Department of Information Systems

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Resource Consumption and Development Effort in GPU-Optimized Industrial Anomaly Detection Inference: A Comparison between PyTorch and a C++/CUDA Pipeline

Thesis Relevance	Bachelor
Thesis Supervisor	Dr. Nina Herrmann
Associated Professor	Dr. Nina Herrmann
Thesis Status	Completed