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Product Description
In machine learning, an Encoder Coupling refers to a technique utilized in generative models like normalizing flows, to improve the flexibility and efficiency of the model. This technique comprises separating the encoder network into two parts: the conditional and the non-conditional. The conditional part encodes the input data whereas the non-conditional part has the learned parameters of the model. By decoupling these two parts, the model generates a large range of outputs utilizing only a single set of parameters that allow for efficient sampling as well as improved model performance. Besides, Encoder Coupling can be bought at the most reasonable rates.