Artificial intelligence deciphers detector 'clouds' to accelerate materials research arxiv
randomly chosen from the range . This process can be conceptualized as for a given frame, choosing a specific range with probability 1/3 and then choosing a specificvalue from that range. This approach was chosen to ensure good coverage within the full range ofFor the validation and test datasets, a slightly different procedure was chosen. Since our ultimate metric is contrast , our validation and test data are carefully chosen to reflect this.
Using these predicted contrasts for different contrast levels, we form a contrast–contrast parity plot for the ground truth contrast vs predicted contrast. To select between competing trained models, the optimal neural network was selected based on maximizing the correlation between the estimated and the true contrast in the contrast–contrast parity plot. Specifically, for the lowdata, we calculate the correlation of the 18 validation contrasts relative to the ground truth contrasts.
. In addition to the correlation, the validation and training loss functions were also monitored during the course of training. A sample plot for the full-range
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