US 12,236,352 B2
Transfer learning based on cross-domain homophily influences
Craig M. Trim, Glendale, CA (US); Aaron K. Baughman, Research Triangle Park, NC (US); Garfield W. Vaughn, Southbury, CT (US); and Micah Forster, Austin, TX (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 10, 2023, as Appl. No. 18/349,902.
Application 18/349,902 is a continuation of application No. 16/553,823, filed on Aug. 28, 2019.
Prior Publication US 2023/0359899 A1, Nov. 9, 2023
Int. Cl. G06N 3/086 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/086 (2013.01) [G06N 3/045 (2023.01)] 25 Claims
OG exemplary drawing
 
1. A computer implemented method comprising:
generating a plurality of deep transfer learning networks based on a plurality of first exemplars and a plurality of second exemplars;
encoding one or more transfer layers to a chromosome for genetic operators, where the one or more transfer layers are to be transferred from a source deep transfer learning network corresponding to a source domain to a target deep transfer learning network corresponding to a target domain;
diversifying concurrently both the source deep transfer learning network and the target deep transfer learning network by use of the genetic operators; and
producing the target deep transfer learning network that integrates a result from the diversifying.