Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

multiclass accuracy

Multiclass accuracy is a machine learning evaluation metric that measures the proportion of correct predictions made by a model across a classification problem involving three or more mutually exclusive categories. In standard evaluation, it is calculated as the total number of correctly predicted instances divided by the overall number of samples, reflecting overall model correctness across all classes. In contexts with imbalanced datasets, multiclass accuracy can also be evaluated as the mean of the accuracies achieved on each individual category, ensuring that performance on underrepresented classes contributes equally to the final score.

1 item

An embarrassingly simple approach to zero-shot learning

An embarrassingly simple approach to zero-shot learning

Bernardino Romera-Paredes, Philip H. S. Torr

OrganizationsUniversity of Oxford

Why you should read this

Proposes a two-layer linear framework for zero-shot learning that is implementable in a single line of code, establishes theoretical generalization bounds through domain adaptation, and surpasses complex state-of-the-art methods across standard benchmarks by up to 17%.

Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a two linear layers network, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of art on all of them, obtaining a ratio of improvement up to 17%.

Added

2026-09-25