公开(公告)号：US20200372339A1

公开(公告)日：2020-11-26

申请号：US16592474

申请日：2019-10-03

Applicant: salesforce.com, inc.

Inventor： Tong Che ,  Caiming Xiong

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  H03M7/30 ,  G06F17/18

Abstract: Verification of discriminative models includes receiving an input; receiving a prediction from a discriminative model for the input; encoding, using an encoder, a latent variable based on the input; decoding, using a decoder, a reconstructed input based on the prediction and the latent variable; and determining, using an anomaly detection module, whether the prediction is reliable based on the input, the reconstructed input, and the latent variable. The encoder and the decoder are jointly trained to maximize an evidence lower bound of the encoder and the decoder. In some embodiments, the encoder and the decoder are further trained using a disentanglement constraint between the prediction and the latent variable. In some embodiments, the encoder and the decoder are further trained without using inputs that are out of a distribution of inputs used to train the discriminative model or that are adversarial to the discriminative model.

公开(公告)号：US11657269B2

公开(公告)日：2023-05-23

申请号：US16592474

申请日：2019-10-03

Applicant: salesforce.com, inc.

Inventor： Tong Che ,  Caiming Xiong

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06F17/18 ,  H03M7/30

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06N3/0454 ,  G06N20/20 ,  H03M7/3059 ,  H03M7/6005 ,  H03M7/6011

Abstract: Verification of discriminative models includes receiving an input; receiving a prediction from a discriminative model for the input; encoding, using an encoder, a latent variable based on the input; decoding, using a decoder, a reconstructed input based on the prediction and the latent variable; and determining, using an anomaly detection module, whether the prediction is reliable based on the input, the reconstructed input, and the latent variable. The encoder and the decoder are jointly trained to maximize an evidence lower bound of the encoder and the decoder. In some embodiments, the encoder and the decoder are further trained using a disentanglement constraint between the prediction and the latent variable. In some embodiments, the encoder and the decoder are further trained without using inputs that are out of a distribution of inputs used to train the discriminative model or that are adversarial to the discriminative model.