Amplification
Amplification is a technique in machine learning that involves generating training data from existing data by adding noise or perturbations to the input data. The goal of amplification is to increase the diversity of the training data, which can improve the robustness and generalization of the resulting machine learning model. Amplification can be used in various machine learning tasks, such as image classification, natural language processing, and speech recognition. One common approach to amplification is to use generative models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), to generate new data that is similar to the original data but with some variations. Another approach is to use data augmentation techniques, such as random cropping, flipping, or rotation, to create new training examples from the existing ones.
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