# Few-Shot Learning with Geometric Constraints

arXiv: https://arxiv.org/abs/2003.09151 · HTML: https://mlsanity.com/paper/2003.09151/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Few-Shot Object Detection: A Comprehensive Survey](https://mlsanity.com/paper/2112.11699/) (2021)
- [Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2301.09964/) (2023)
- [Disentangled Feature Representation for Few-shot Image Classification](https://mlsanity.com/paper/2109.12548/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [Spatial Reasoning for Few-Shot Object Detection](https://mlsanity.com/paper/2211.01080/) (2022)
- [Few-Shot Anomaly Detection via Category-Agnostic Registration Learning](https://mlsanity.com/paper/2406.08810/) (2024)
- [SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2110.04494/) (2021)
- [GC-TTS: Few-shot Speaker Adaptation with Geometric Constraints](https://mlsanity.com/paper/2108.06890/) (2021)
- [Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition](https://mlsanity.com/paper/2209.03034/) (2022)
- [Joint Neural Networks for One-shot Object Recognition and Detection](https://mlsanity.com/paper/2408.00701/) (2024)
- [DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2607.04665/) (2026)
- [Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning](https://mlsanity.com/paper/2607.07117/) (2026)

## Abstract (arXiv)

In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we aim to add novel categories to it that have only a few, e.g., one or five, training examples. This is a challenging scenario because: 1) high performance is required in both the base and novel categories; and 2) training the network for the new categories with a few training examples can contaminate the feature space trained well for the base categories. To address these challenges, we propose two geometric constraints to fine-tune the network with a few training examples. The first constraint enables features of the novel categories to cluster near the category weights, and the second maintains the weights of the novel categories far from the weights of the base categories. By applying the proposed constraints, we extract discriminative features for the novel categories while preserving the feature space learned for the base categories. Using public data sets for few-shot learning that are subsets of ImageNet, we demonstrate that the proposed method outperforms prevalent methods by a large margin.