Unbiased feature generating for generalized zero-shot learning

Generalized zero-shot learning (GZSL) aims at training a model on seen data to recognize objects from both seen and unseen classes. Existing generated-based methods show encouraging performance by directly generating unseen samples. However, due to insufficient exploration of unseen label space and limited class-wise semantic descriptions, existing methods still face the bias problem. In this paper, we divide the bias problem into seen-biased and neighbor-biased problems and propose a GZSL method named Unbiased Feature Generating. For the seen-biased problem, we train a classifier in complete label space by introducing the discriminative information contained in fake unseen samples. For the neighbor-biased problem, we generate untypical samples and refine the classification boundaries among neighbor classes. The classifier in complete label space and generator are trained in an iterative process to complement each other. The experimental results on four widely used datasets verify our method achieves encouraging performance compared with the state-of-the-art methods.