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Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Zero-shot learning transfers knowledge from seen classes to novel unseen...
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Probabilistic Zero-shot Classification with Semantic Rankings
In this paper we propose a non-metric ranking-based representation of se...
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Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-l...
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Generalized Zero-shot ICD Coding
The International Classification of Diseases (ICD) is a list of classifi...
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Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification
Zero-shot learning (ZSL) is one of the most extreme forms of learning fr...
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Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
Multi-label zero-shot classification aims to predict multiple unseen cla...
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Zero-Shot Recognition through Image-Guided Semantic Classification
We present a new embedding-based framework for zero-shot learning (ZSL)....
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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.
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