# One-Shot Image Classification by Learning to Restore Prototypes

arXiv: https://arxiv.org/abs/2005.01234 · HTML: https://mlsanity.com/paper/2005.01234/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?](https://mlsanity.com/paper/2401.12902/) (2024)
- [Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions](https://mlsanity.com/paper/2202.11423/) (2022)
- [MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning](https://mlsanity.com/paper/2103.14341/) (2021)
- [Prototype Completion for Few-Shot Learning](https://mlsanity.com/paper/2108.05010/) (2021)
- [GDC- Generalized Distribution Calibration for Few-Shot Learning](https://mlsanity.com/paper/2204.05230/) (2022)
- [Exploring Category-correlated Feature for Few-shot Image Classification](https://mlsanity.com/paper/2112.07224/) (2021)
- [DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation](https://mlsanity.com/paper/2307.15317/) (2023)
- [Few-Shot Class Incremental Learning via Robust Transformer Approach](https://mlsanity.com/paper/2405.05984/) (2024)
- [FedSC: Federated Learning with Semantic-Aware Collaboration](https://mlsanity.com/paper/2506.21012/) (2025)
- [Incremental Few-Shot Learning via Implanting and Compressing](https://mlsanity.com/paper/2203.10297/) (2022)
- [Disentangled Generation with Information Bottleneck for Few-Shot Learning](https://mlsanity.com/paper/2211.16185/) (2022)
- [P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification](https://mlsanity.com/paper/2301.00740/) (2023)
- [Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification](https://mlsanity.com/paper/2311.01212/) (2023)

## Abstract (arXiv)

One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.