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Found 2 papers, 1 papers with code
Driver2vec: Driver Identification from Automotive Data
In this paper, we develop a deep learning architecture (Driver2vec) to map a short interval of driving data into an embedding space that represents the driver's behavior to assist in driver identification.
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
In this work, we propose MoCo-CXR, which is an adaptation of the contrastive learning method Momentum Contrast (MoCo), to produce models with better representations and initializations for the detection of pathologies in chest X-rays.
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