Latent Space
In data science and artificial intelligence, Latent Space refers to the space of encoded features extracted from raw data using techniques such as dimensionality reduction, autoencoders, or generative models. The term 'latent' refers to the fact that these features are not directly observable in the input data, but rather are inferred by the model. Latent Space is often used in unsupervised learning tasks such as clustering, anomaly detection, and data visualization. By projecting high-dimensional data into a lower-dimensional Latent Space, it becomes possible to identify patterns and relationships that are not easily discernible in the original data. Latent Space can also be used for generating new data samples that are similar to the original data, by sampling from the learned distribution in the Latent Space.
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