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Achieving up to 96% accuracy in parking space classification with minimal annotated data could revolutionize intelligent transportation systems.
Unsupervised threshold determination in Siamese networks can match traditional methods' accuracy while drastically reducing the need for labeled data.
Self-supervised learning can achieve over 97% accuracy in parking spot occupancy recognition without requiring labeled data from the target environment.
