Monte-carlo adversarial autoencoder for multi-source domain adaptation

Abstract

Embodiments may include novel techniques for training and using an adversarial autoencoder for multi-source domain functions. For example, a method may comprise training an adversarial encoder comprising an encoder and a decoder by simultaneously training the encoder and the decoder, using data comprising a plurality of datasets, the data having labels based on an origin class and a dataset number, training the encoder to act as a generator to generate codewords based on the data for a generative adversarial network including the generator and a discriminator by training the generator to cause the discriminator to predict random labels for a plurality of data samples of each class and training the generator using the predicted random labels to generate codewords that relate to the origin class, and classifying new data samples using the trained adversarial encoder and generator, and the discriminator.