Invention Grant
- Patent Title: Certifiably robust interpretation
-
Application No.: US17005144Application Date: 2020-08-27
-
Publication No.: US11687777B2Publication Date: 2023-06-27
- Inventor: Ao Liu , Sijia Liu , Bo Wu , Lirong Xia , Qi Cheng Li , Chuang Gan
- Applicant: International Business Machines Corporation , Rensselaer Polytechnic Institute
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation,Rensselaer Polytechnic Institute
- Current Assignee: International Business Machines Corporation,Rensselaer Polytechnic Institute
- Current Assignee Address: US NY Armonk; US NY Troy
- Agency: Michael J. Chang, LLC
- Agent Anthony Curro
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F16/56 ; G06T3/40 ; G06T5/00 ; G06F18/21

Abstract:
Interpretation maps of convolutional neural networks having certifiable robustness using Rényi differential privacy are provided. In one aspect, a method for generating an interpretation map includes: adding generalized Gaussian noise to an image x to obtain T noisy images, wherein the generalized Gaussian noise constitutes perturbations to the image x; providing the T noisy images as input to a convolutional neural network; calculating T noisy interpretations of output from the convolutional neural network corresponding to the T noisy images; re-scaling the T noisy interpretations using a scoring vector υ to obtain T re-scaled noisy interpretations; and generating the interpretation map using the T re-scaled noisy interpretations, wherein the interpretation map is robust against the perturbations.
Public/Granted literature
- US20220067505A1 Certifiably Robust Interpretation Public/Granted day:2022-03-03
Information query