An autoencoder is a deep learning architecture that learns compressed representations of data by reconstructing inputs through an encoder-decoder structure. In cybersecurity and computer vision, autoencoders (including variational and convolutional variants) have been applied to insider threat detection and adversarial-perturbation denoising. Autoencoders have also been used inside neural program optimization pipelines, where they are reported to suffer from local-minima issues that prompt alternative problem formulations.

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