# Zero-shot Learning with Class Description Regularization

arXiv: https://arxiv.org/abs/2106.16108 · HTML: https://mlsanity.com/paper/2106.16108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild](https://mlsanity.com/paper/1605.04253/) (2016)
- [A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts](https://mlsanity.com/paper/1712.01381/) (2017)
- [Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision](https://mlsanity.com/paper/1709.01148/) (2017)
- [Creativity Inspired Zero-Shot Learning](https://mlsanity.com/paper/1904.01109/) (2019)
- [From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis](https://mlsanity.com/paper/1705.01782/) (2017)

## Built on by (most cited first, 2 of 2)

- [PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck](https://mlsanity.com/paper/2403.05297/) (2024)
- [Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions](https://mlsanity.com/paper/2303.04053/) (2023)

## Abstract (arXiv)

The purpose of generative Zero-shot learning (ZSL) is to learning from seen classes, transfer the learned knowledge, and create samples of unseen classes from the description of these unseen categories. To achieve better ZSL accuracies, models need to better understand the descriptions of unseen classes. We introduce a novel form of regularization that encourages generative ZSL models to pay more attention to the description of each category. Our empirical results demonstrate improvements over the performance of multiple state-of-the-art models on the task of generalized zero-shot recognition and classification when trained on textual description-based datasets like CUB and NABirds and attribute-based datasets like AWA2, aPY and SUN.