Triple Verification Network for Generalised Zero-shot Learning.

Related Articles

Triple Verification Network for Generalised Zero-shot Learning.

IEEE Trans Image Process. 2018 Sep 12;:

Authors: Zhang H, Long Y, Guan Y, Shao L

Conventional Zero-shot Learning approaches often suffer from severe performance degradation in the Generalised Zero-shot Learning (GZSL) scenario, i.e. to recognise test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a Triple Verification problem and propose a unified optimisation of regression and compatibility functions, i.e. two main streams of existing ZSL approaches. The complementary losses mutually regularise the same model to mitigate the CO problem. Furthermore, we implement a deep extension paradigm to linear models and significantly outperforms state-of-the-art methods in both GZSL and ZSL scenarios on the four standard benchmarks.

PMID: 30222566 [PubMed – as supplied by publisher]

Source link

WordPress database error: [Error writing file '/tmp/MYHq2ouo' (Errcode: 28 - No space left on device)]
SELECT SQL_CALC_FOUND_ROWS wp_posts.ID FROM wp_posts LEFT JOIN wp_term_relationships ON (wp_posts.ID = wp_term_relationships.object_id) WHERE 1=1 AND wp_posts.ID NOT IN (63597) AND ( wp_term_relationships.term_taxonomy_id IN (313) ) AND wp_posts.post_type = 'post' AND (wp_posts.post_status = 'publish') GROUP BY wp_posts.ID ORDER BY RAND() LIMIT 0, 3

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy