公开(公告)号：US20220156530A1

公开(公告)日：2022-05-19

申请号：US17188232

申请日：2021-03-01

Applicant: salesforce.com, inc.

Inventor： Anthony Meng Huat Tiong ,  Junnan Li ,  Chu Hong Hoi

IPC: G06K9/62 ,  G06N3/08

Abstract: An interpolative centroid contrastive learning (ICCL) framework is disclosed for learning a more discriminative representation for tail classes. Specifically, data samples, such as natural images, are projected into a low-dimensional embedding space, and class centroids for respective classes are created as average embeddings of samples that belong to a respective class. Virtual training samples are then created by interpolating two images from two samplers: a class-agnostic sampler which returns all images from both the head class and the tail class with an equal probability, and a class-aware sampler which focuses more on tail-class images by sampling images from the tail class with a higher probability compared to images from the head class. The sampled images, e.g., images from the class-agnostic sampler and images from the class-aware sampler may be interpolated to generate interpolated images.