Fitting latent manifold (IMAGE)
Caption
Figure 1: Illustration of fitting the latent manifold using the Cycle Generative Adversarial Network (CycleGAN). CycleGAN is a deep learning technique for unsupervised image-to-image translation. In the real world, data, such as the images shown in panel (a), are often high-dimensional vectors. These vectors typically reside around a low-dimensional latent manifold, depicted by the black dotted curve in panel (b). The CycleGAN framework, detailed in panel (c), effectively learns to estimate this latent manifold (illustrated as the red curve in panel (b)). This advancement facilitates nonlinear interpolation and denoising within the high-dimensional ambient space (panel (d)), offering significant improvements in data processing and analysis.
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National University of Singapore
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