# AutoEncoder - ![[Pasted image 20220310204652.webp]] - Regression by predicting a reconstruction of the data - Encoder $E : \mathscr{X} \rightarrow \mathscr{F}$ - Decoder $\mathscr{F} \rightarrow \mathscr{D}$ - $E_\theta, D_\theta = argmin_{E_\theta, D\theta}||X-D(E(X))||^2$ - Learn using [[Gradient Descent]] - Compressed rep of data -> Good for Classification or Regression - [[MSE]] : Unsupervised ## Difficulties - dim $\mathscr{F} \lt \mathscr{X}$ - Cannot learn the identity function - usages - data compression / [[Dimensionality Reduction]] - encoder to obtain features (use the latent variable as feature) - denoising autoencoders - input noisy image and try to obtain image without noise - sparse auto-encoder - contractive autoencoder ## Types - [[Denoising Autoencoder]] - [[VAE]]