Not Every Model Has a Separate "Loss Function"
Read OriginalThis technical article critiques the common deep learning design pattern of forcing a separate 'loss function' abstraction. It argues this separation is not always optimal, leads to code duplication, and limits flexibility, especially for complex models like multi-task networks. The author proposes an alternative design where the model itself can compute the loss, offering a cleaner and more general-purpose system architecture.
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Not Every Model Has a Separate "Loss Function"
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