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Contractive Autoencoder (CAE) The theory at the rear of a contractive autoencoder, proposed by Rifai et al. [ninety], is to make the autoencoders robust of little modifications inside the education dataset. In its goal function, a CAE involves an express regularizer that forces the product to learn an encoding that is robust to compact modifications in enter values.
Subsequently, the acquired representation’s sensitivity to the training input is reduced. While DAEs inspire the robustness of reconstruction as discussed above, CAEs encourage the robustness of illustration.
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