Gradient Matching Generative Networks for Zero-Shot Learning

Date

2019-01-01

Author

Sariyildiz, Mert Bulent
Cinbiş, Ramazan Gökberk

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Zero-shot learning (ZSL) is one of the most promising problems where substantial progress can potentially be achieved through unsupervised learning, due to distributional differences between supervised and zero-shot classes. For this reason, several works investigate the incorporation of discriminative domain adaptation techniques into ZSL, which, however, lead to modest improvements in ZSL accuracy. In contrast, we propose a generative model that can naturally learn from unsupervised examples, and synthesize training examples for unseen classes purely based on their class embeddings, and therefore, reduce the zero-shot learning problem into a supervised classification task. The proposed approach consists of two important components: (i) a conditional Generative Adversarial Network that learns to produce samples that mimic the characteristics of unsupervised data examples, and (ii) the Gradient Matching (GM) loss that measures the quality of the gradient signal obtained from the synthesized examples. Using our GM loss formulation, we enforce the generator to produce examples from which accurate classifiers can be trained. Experimental results on several ZSL benchmark datasets show that our approach leads to significant improvements over the state of the art in generalized zero-shot classification.

Subject Keywords

Computer science, Zero-shot learning (ZSL), Generative adversarial network, Gradient Matching (GM)

URI

https://hdl.handle.net/11511/39265

DOI

https://doi.org/10.1109/cvpr.2019.00227

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. B. Sariyildiz and R. G. Cinbiş, “Gradient Matching Generative Networks for Zero-Shot Learning,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39265.