Transductive Multi-class and Multi-label Zero-shot Learning
This work addresses the challenge of recognizing unseen classes in machine learning, particularly for multi-class and multi-label tasks, but appears incremental as it builds on existing ZSL methods.
The paper tackles the problem of zero-shot learning (ZSL) by improving conventional approaches through transductive learning and extending it to multi-label scenarios, aiming to enhance knowledge transfer between auxiliary and target datasets.
Recently, zero-shot learning (ZSL) has received increasing interest. The key idea underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate-level semantic representation which is assumed to be shared between the auxiliary and target datasets, and is used to bridge between these domains for knowledge transfer. The semantic representation used in existing approaches varies from visual attributes to semantic word vectors and semantic relatedness. However, the overall pipeline is similar: a projection mapping low-level features to the semantic representation is learned from the auxiliary dataset by either classification or regression models and applied directly to map each instance into the same semantic representation space where a zero-shot classifier is used to recognise the unseen target class instances with a single known 'prototype' of each target class. In this paper we discuss two related lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the multi-label case.