Latent space refers to a representation of compressed data in a lower-dimensional space, often used in machine learning and deep learning models. In this context, it captures the essential features or patterns of the input data while discarding irrelevant details. Latent spaces are crucial for tasks such as generative modeling, where new data points can be synthesized by sampling from this space. Common use cases include Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), where the latent space allows for the generation of new, realistic data samples that resemble the training data.
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AI Fundamentals