# Generative Adversarial Residual Pairwise Networks for One Shot Learning

arXiv: https://arxiv.org/abs/1703.08033 · HTML: https://mlsanity.com/paper/1703.08033/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Matching Feature Sets for Few-Shot Image Classification](https://mlsanity.com/paper/2204.00949/) (2022)
- [Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition](https://mlsanity.com/paper/2408.00249/) (2024)
- [Few-shot Object Detection with Refined Contrastive Learning](https://mlsanity.com/paper/2211.13495/) (2022)
- [Learning More Discriminative Local Descriptors for Few-shot Learning](https://mlsanity.com/paper/2305.08721/) (2023)
- [Learning Compositional Representations for Effective Low-Shot Generalization](https://mlsanity.com/paper/2204.08090/) (2022)
- [CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2412.12654/) (2024)
- [Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets](https://mlsanity.com/paper/2210.07076/) (2022)
- [Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition](https://mlsanity.com/paper/2505.06002/) (2025)
- [Hyperbolic Coarse-to-Fine Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2509.18504/) (2025)
- [Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures](https://mlsanity.com/paper/2601.13059/) (2026)

## Abstract (arXiv)

Deep neural networks achieve unprecedented performance levels over many tasks and scale well with large quantities of data, but performance in the low-data regime and tasks like one shot learning still lags behind. While recent work suggests many hypotheses from better optimization to more complicated network structures, in this work we hypothesize that having a learnable and more expressive similarity objective is an essential missing component. Towards overcoming that, we propose a network design inspired by deep residual networks that allows the efficient computation of this more expressive pairwise similarity objective. Further, we argue that regularization is key in learning with small amounts of data, and propose an additional generator network based on the Generative Adversarial Networks where the discriminator is our residual pairwise network. This provides a strong regularizer by leveraging the generated data samples. The proposed model can generate plausible variations of exemplars over unseen classes and outperforms strong discriminative baselines for few shot classification tasks. Notably, our residual pairwise network design outperforms previous state-of-theart on the challenging mini-Imagenet dataset for one shot learning by getting over 55% accuracy for the 5-way classification task over unseen classes.