adversarial feature learning
Adversarial feature learning refers to a technique in machine learning where a model is trained to extract discriminative features from data by learning to distinguish between real and generated samples. It involves training two models – one generating synthetic samples and the other trying to accurately distinguish between the real and generated samples. This adversarial process helps the model to capture more informative and relevant features from the data.
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