StandardAutoencodern

StandardAutoencodern is a baseline neural network model used in unsupervised representation learning. It consists of an encoder that maps an input vector x in R^D to a latent representation z in R^L, followed by a decoder that reconstructs x_hat from z. The architecture is typically symmetric, with layers that reduce dimensionality to L and then expand back to D. Hidden layers commonly use ReLU activations, with a sigmoid or linear output layer depending on the data type.

Training objective: the model is trained to minimize a reconstruction loss, such as mean squared error for continuous data or cross-entropy for binary data, over a dataset of samples. Regularization techniques like weight decay or sparsity penalties may be applied to encourage compact representations. The StandardAutoencodern is deterministic, in contrast to probabilistic autoencoders, and its latent variables are explicit outputs of the encoder.

Variants and relation: Several variants are discussed alongside it, including denoising autoencoders, which corrupt the input during training, and contractive autoencoders that penalize the Jacobian of the encoder. The StandardAutoencodern serves as a straightforward baseline for comparing dimensionality reduction and pretraining approaches.

Applications and limitations: It is used for dimensionality reduction, feature learning, and as a lightweight pretraining step for deeper networks. Limitations include a tendency to learn identity mappings if the bottleneck is not constraining, and limited ability to model complex data distributions without extensions.

History: The approach traces to early autoencoder research and remains a common reference point in tutorials and benchmarks.