Systems and methods for encoding and classifying data

A system for training a machine learning model, the system comprising one or more processors and a memory storing one or more computer programs including instructions, which when executed by the one or more processors, cause the system to:

• receive first training data, by a variational autoencoder (VAE) comprising a probabilistic encoder and a probabilistic decoder;
• and
• encode the first training data, by the probabilistic encoder, to generate encoded data in an embedding space;
• decode the encoded data, by the probabilistic decoder, to generate decoded data;
• compute a loss function, comprising a hinge-loss term, based on the decoded data and the encoded data;
• and
• adjust one or more parameters of the VAE based on the computed loss function including the hinge-loss term.

A non-transitory computer-readable storage medium for training a machine learning model, the non-transitory computer-readable storage medium storing instructions configured to be executed by one or more processors of a system to cause the system to:

• receive first training data, by a variational autoencoder (VAE) comprising a probabilistic encoder and a probabilistic decoder;
• and
• encode the first training data, by the probabilistic encoder, to generate encoded data in an embedding space;
• decode the encoded data, by the probabilistic decoder, to generate decoded data;
• compute a loss function, comprising a hinge-loss term, based on the decoded data and the encoded data;
• and
• adjust one or more parameters of the VAE based on the computed loss function including the hinge-loss term.

A system for classifying encoded data, the system comprising one or more processors and a memory storing one or more computer programs including instructions, which when executed by the one or more processors, cause the system to:

• receive, by a probabilistic encoder of a variational autoencoder (VAE), first input data;
• generate, by the probabilistic encoder based on the input data, encoded data in an embedding space, wherein the encoded data in the embedding space comprises a first class of encoded data separated by a hyperplane from a second class of encoded data;
• and
• classify, by a linear support vector machine (SVM), the encoded data in the embedding space wherein classifying the encoded data comprises determining the hyperplane in the embedding space separating the first class of the encoded data from the second class of the encoded data.

A method for classifying encoded data, the method comprising:

• receiving, by a probabilistic encoder of a variational autoencoder (VAE), first input data;
• generating, by the probabilistic encoder based on the input data, encoded data in an embedding space, wherein the encoded data in the embedding space comprises a first class of encoded data separated by a hyperplane from a second class of encoded data;
• and
• classifying, by a linear support vector machine (SVM), the encoded data in the embedding space wherein classifying comprises determining the hyperplane in the embedding space separating the first class of the encoded data from the second class of the encoded data.

A non-transitory computer-readable storage medium for training a machine learning model, the non-transitory computer-readable storage medium storing instructions configured to be executed by one or more processors of a system to cause the system to:

• receive, by a probabilistic encoder of a variational autoencoder (VAE), first input data;
• generate, by the probabilistic encoder based on the input data, encoded data in an embedding space, wherein the encoded data in the embedding space comprises a first class of encoded data separated by a hyperplane from a second class of encoded data;
• and
• classify, by a linear support vector machine (SVM), the encoded data in the embedding space wherein classifying comprises determining the hyperplane in the embedding space separating the first class of the encoded data from the second class of the encoded data.