BPU-Net: a precise grain image segmentation method based on bidirectional skip connections and continuous VI loss

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// Synchronous source from in-memory data

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Trained — weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search.