Corpus ID: 236318260

LARGE: Latent-Based Regression through GAN Semantics

  title={LARGE: Latent-Based Regression through GAN Semantics},
  author={Yotam Nitzan and Rinon Gal and Ofir Brenner and Daniel Cohen-Or},
We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner. These directions have been widely used in GAN-based image… Expand
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