# Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning

arXiv: https://arxiv.org/abs/1810.08332 · HTML: https://mlsanity.com/paper/1810.08332/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 15 of 15)

- [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021)
- [Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection](https://mlsanity.com/paper/2103.01903/) (2021)
- [TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2112.08643/) (2021)
- [Mask-guided BERT for Few Shot Text Classification](https://mlsanity.com/paper/2302.10447/) (2023)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [Attribute Prototype Network for Any-Shot Learning](https://mlsanity.com/paper/2204.01208/) (2022)
- [SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning](https://mlsanity.com/paper/2111.04316/) (2021)
- [Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis](https://mlsanity.com/paper/2203.05335/) (2022)
- [Prototype Completion for Few-Shot Learning](https://mlsanity.com/paper/2108.05010/) (2021)
- [Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning](https://mlsanity.com/paper/2109.01295/) (2021)
- [Reconstruction guided Meta-learning for Few Shot Open Set Recognition](https://mlsanity.com/paper/2108.00340/) (2021)
- [PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning](https://mlsanity.com/paper/2410.11560/) (2024)
- [Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning](https://mlsanity.com/paper/2406.03032/) (2024)
- [Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04963/) (2025)
- [Few-Shot Inspired Generative Zero-Shot Learning](https://mlsanity.com/paper/2507.01026/) (2025)

## Abstract (arXiv)

Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g.,~an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by unseen class data synthesis and robust projection function learning. Specifically, a novel semantic data synthesis strategy is proposed, by which semantic class prototypes (e.g., attribute vectors) are used to simply perturb seen class data for generating unseen class ones. As in any data synthesis/hallucination approach, there are ambiguities and uncertainties on how well the synthesised data can capture the targeted unseen class data distribution. To cope with this, the second contribution of this work is a novel projection learning model termed competitive bidirectional projection learning (BPL) designed to best utilise the ambiguous synthesised data. Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion. As a third contribution, we show that the proposed ZSL model can be easily extended to few-shot learning (FSL) by again exploiting semantic (class prototype guided) feature synthesis and competitive BPL. Extensive experiments show that our model achieves the state-of-the-art results on both problems.