# From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

arXiv: https://arxiv.org/abs/1705.01782 · HTML: https://mlsanity.com/paper/1705.01782/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2017.

## Built on by (most cited first, 21 of 21)

- [Fine-Grained Zero-Shot Learning with DNA as Side Information](https://mlsanity.com/paper/2109.14133/) (2021)
- [Mitigating Generation Shifts for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2107.03163/) (2021)
- [GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2207.01798/) (2022)
- [Disentangling Semantic-to-visual Confusion for Zero-shot Learning](https://mlsanity.com/paper/2106.08605/) (2021)
- [Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis](https://mlsanity.com/paper/2203.05335/) (2022)
- [Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis](https://mlsanity.com/paper/2501.03565/) (2025)
- [The Curious Layperson: Fine-Grained Image Recognition without Expert Labels](https://mlsanity.com/paper/2111.03651/) (2021)
- [CIZSL++: Creativity Inspired Generative Zero-Shot Learning](https://mlsanity.com/paper/2101.00173/) (2021)
- [Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization](https://mlsanity.com/paper/2101.09892/) (2021)
- [Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs](https://mlsanity.com/paper/2107.06563/) (2021)
- [Vocabulary-informed Zero-shot and Open-set Learning](https://mlsanity.com/paper/2301.00998/) (2023)
- [Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure](https://mlsanity.com/paper/2403.09363/) (2024)
- [Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation](https://mlsanity.com/paper/2104.09757/) (2021)
- [Zero-shot Learning with Class Description Regularization](https://mlsanity.com/paper/2106.16108/) (2021)
- [Absolute Zero-Shot Learning](https://mlsanity.com/paper/2202.11319/) (2022)
- [Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2204.05666/) (2022)
- [Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning](https://mlsanity.com/paper/2212.13151/) (2022)
- [Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning](https://mlsanity.com/paper/2102.11566/) (2021)
- [Structure-Aware Feature Generation for Zero-Shot Learning](https://mlsanity.com/paper/2108.07032/) (2021)
- [Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning](https://mlsanity.com/paper/2304.13963/) (2023)
- [DeepKnowledge: Generalisation-Driven Deep Learning Testing](https://mlsanity.com/paper/2403.16768/) (2024)

## Abstract (arXiv)

Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting sufficient images for ever-growing new classes is unattainable. In this paper, we propose a new Zero-shot learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage. Hereafter, ZSL recognition is converted into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical classifiers such as SVM. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data. Extensive experimental results suggest that our proposed approach significantly improve the state-of-the-art results.