:ref:`Version française ` .. _dae_en: Denoising autoencoders vs. ordinary autoencoders ================================================ A denoising autoencoder is like an ordinary autoencoder, with the difference that during learning, the input seen by the autoencoder is not the raw input but a stochastically corrupted version. A denoising autoencoder is thus trained to *reconstruct* the original input from the noisy version. For more information see the article from ICML 2008: `Denoising Auto-Encoders `_. Principal differences between ordinary autoencoders and denoising autoencoders: .. list-table:: Comparison of ordinary/denoising autoencoder :widths: 20 30 30 :header-rows: 1 * - Aspect - Ordinary autoencoders - Denoising autoencoders * - What it does - Finds a compact representation - Capture the joint distribution of the inputs * - Learning criterion - Deterministic - Stochastic * - Number of hidden units - Must be limited to avoid learning the identity function - As many as are necessary for capturing the distribution * - How to choose model capacity (i.e. the number of hidden units) - Impossible using standard reconstruction error, since it will always be lower with more hidden units - Can use the mean reconstruction error * - Choosing the number of learning iterations - Impossible using reconstruction error: use classification error after supervised fine-tuning - Can do early stopping using the mean reconstruction error * - Choosing the amount of corrupting noise - not applicable - Cannot use reconstruction error: use classification error after supervised fine-tuning