# Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images

arXiv: https://arxiv.org/abs/1904.08482 · HTML: https://mlsanity.com/paper/1904.08482/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://mlsanity.com/paper/2302.03133/) (2023)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [Matching Feature Sets for Few-Shot Image Classification](https://mlsanity.com/paper/2204.00949/) (2022)
- [Prototype Guided Federated Learning of Visual Feature Representations](https://mlsanity.com/paper/2105.08982/) (2021)
- [Few-Shot Object Detection via Variational Feature Aggregation](https://mlsanity.com/paper/2301.13411/) (2023)
- [EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation](https://mlsanity.com/paper/2110.06679/) (2021)
- [Federated Semi-Supervised Learning with Prototypical Networks](https://mlsanity.com/paper/2205.13921/) (2022)
- [Sill-Net: Feature Augmentation with Separated Illumination Representation](https://mlsanity.com/paper/2102.03539/) (2021)
- [Informed Pre-Training on Prior Knowledge](https://mlsanity.com/paper/2205.11433/) (2022)
- [Worst Case Matters for Few-Shot Recognition](https://mlsanity.com/paper/2203.06574/) (2022)
- [Decomposed Prototype Learning for Few-Shot Scene Graph Generation](https://mlsanity.com/paper/2303.10863/) (2023)
- [Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds](https://mlsanity.com/paper/2503.17677/) (2025)
- [Reconstruction guided Meta-learning for Few Shot Open Set Recognition](https://mlsanity.com/paper/2108.00340/) (2021)
- [ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation](https://mlsanity.com/paper/2302.09765/) (2023)
- [Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning](https://mlsanity.com/paper/2309.02088/) (2023)
- [FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation](https://mlsanity.com/paper/2402.18292/) (2024)
- [AutoProtoNet: Interpretability for Prototypical Networks](https://mlsanity.com/paper/2204.00929/) (2022)
- [Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets](https://mlsanity.com/paper/2210.07076/) (2022)
- [Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification](https://mlsanity.com/paper/2211.12926/) (2022)
- [Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer](https://mlsanity.com/paper/2309.08851/) (2023)
- [Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization](https://mlsanity.com/paper/2508.12927/) (2025)

## Abstract (arXiv)

In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an open-set graphic symbol recognition problem by one-shot classification with prototypical images as a single training example for each novel class. We take an approach to learn a generalizable embedding space for novel tasks. We propose a new approach called variational prototyping-encoder (VPE) that learns the image translation task from real-world input images to their corresponding prototypical images as a meta-task. As a result, VPE learns image similarity as well as prototypical concepts which differs from widely used metric learning based approaches. Our experiments with diverse datasets demonstrate that the proposed VPE performs favorably against competing metric learning based one-shot methods. Also, our qualitative analyses show that our meta-task induces an effective embedding space suitable for unseen data representation.