Generating low-distortion, in-distribution neighborhood samples of an instance of a dataset using a variational autoencoder

A computer program product for utilizing a variational autoencoder for neighborhood sampling, the computer program product comprising one or more computer readable storage mediums having program code embodied therewith, the program code comprising programming instructions for:

• training said variational autoencoder to generate in-distribution neighborhood samples;
• generating, using said trained variational autoencoder, in-distribution neighborhood samples of an instance of a dataset in a latent space that satisfies a distortion constraint;
• and
• generating a set of interpretable examples for said in-distribution neighborhood samples using a k-nearest neighbors algorithm.

A system, comprising:

• a memory for storing a computer program for utilizing a variational autoencoder for neighborhood sampling;
• and
• a processor connected to said memory, wherein said processor is configured to execute program instructions of the computer program comprising:
• training said variational autoencoder to generate in-distribution neighborhood samples;
• generating, using said trained variational autoencoder, in-distribution neighborhood samples of an instance of a dataset in a latent space that satisfies a distortion constraint;
• and
• generating a set of interpretable examples for said in-distribution neighborhood samples using a k-nearest neighbors algorithm.