Euclidean

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),…

Part 5: A Gentle Introduction to Euclidean Vector SpacePhoto by Karsten Würth on UnsplashMost of the time in both machine learning and deep learning, we are working with vectors. And the vector space model can represent the relationship between data as vectors. Moreover, from a geometric perspective, it is also able to compare the similarity of two vectors either using the distance between the two vectors (euclidean distance) or the angle between two vectors (cosine similarity).Let’s begin with the geometry of the vector…