# From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process

arXiv: https://arxiv.org/abs/1809.10120 · HTML: https://mlsanity.com/paper/1809.10120/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Learning Semantic Ambiguities for Zero-Shot Learning](https://mlsanity.com/paper/2201.01823/) (2022)
- [LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning](https://mlsanity.com/paper/2207.12007/) (2022)
- [Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2210.06120/) (2022)
- [Zero-shot Learning with Deep Neural Networks for Object Recognition](https://mlsanity.com/paper/2102.03137/) (2021)
- [Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2407.13715/) (2024)

## Abstract (arXiv)

Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training examples of seen classes. While classical ZSL considers the recognition performance on unseen classes only, generalized zero-shot learning (GZSL) aims at maximizing performance on both seen and unseen classes. In this paper, we propose a new process for training and evaluation in the GZSL setting; this process addresses the gap in performance between samples from unseen and seen classes by penalizing the latter, and enables to select hyper-parameters well-suited to the GZSL task. It can be applied to any existing ZSL approach and leads to a significant performance boost: the experimental evaluation shows that GZSL performance, averaged over eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from 28.2 to 57.1 on AwA2.