Fig. 2

Representation of an Autoencoder. Autoencoders (AE) are deep neural networks where the input and output are the same (in this case, multi-channel pixel intensity values from images of livestock). They consist of an encoder that codes the input in a low dimensional latent space and a decoder that transforms back the input into a regularized version. Variational autoencoders (VAE) generate a probability function instead of a point latent space. Then, random numbers are drawn and transformed into simulated images by the decoder. Applications of AE and VAE to phenomics remain to be explored, but they can be used for unsupervised learning and imputation. The figure of the cow is from www.dreamstime.com