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Some tasks are too heavy for a single neural network to process efficiently. DualDL allows for a "division of labor." For instance, one model can handle while the other handles semantic labeling . This modularity makes it easier to troubleshoot and scale than a single, massive "black box" model. 3. Combining Creativity with Structure

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At its core, DualDL involves the simultaneous or sequential operation of two distinct deep learning models. Unlike a standard ensemble where multiple models do the same thing to average out errors, DualDL models usually have . Think of it like a writer and an editor:

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