While Generative Adversarial Networks (GANs) are primarily known for their ability to generate high-quality synthetic images, their main task is to learn a latent feature representation of real data. In addition, recent improvements to the original GAN allow it to learn a disentangled latent representation, enabling us to obtain semantically meaningful embeddings.
This property could possibly allow GANs to be used as high-level feature extractors. However, the problem is that the original GAN architecture is not invertible or, in other words, it is impossible to project real images into the latent space. This article addresses this issue and attempts to answer whether GANs can extract meaningful features from real images and if they are suitable for downstream tasks.
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