# Image Deformation Meta-Networks for One-Shot Learning

arXiv: https://arxiv.org/abs/1905.11641 · HTML: https://mlsanity.com/paper/1905.11641/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 30 of 56)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Learning What Not to Segment: A New Perspective on Few-Shot Segmentation](https://mlsanity.com/paper/2203.07615/) (2022)
- [Rectifying the Shortcut Learning of Background for Few-Shot Learning](https://mlsanity.com/paper/2107.07746/) (2021)
- [Meta-Learning with Fewer Tasks through Task Interpolation](https://mlsanity.com/paper/2106.02695/) (2021)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition](https://mlsanity.com/paper/2304.00946/) (2023)
- [Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation](https://mlsanity.com/paper/2204.09903/) (2022)
- [MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition](https://mlsanity.com/paper/2305.07214/) (2023)
- [Matching Feature Sets for Few-Shot Image Classification](https://mlsanity.com/paper/2204.00949/) (2022)
- [MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition](https://mlsanity.com/paper/2405.02077/) (2024)
- [MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation](https://mlsanity.com/paper/2206.09667/) (2022)
- [Self-Supervision Can Be a Good Few-Shot Learner](https://mlsanity.com/paper/2207.09176/) (2022)
- [Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning](https://mlsanity.com/paper/2110.09446/) (2021)
- [Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy](https://mlsanity.com/paper/2410.20366/) (2024)
- [Semantic Compositional Learning for Low-shot Scene Graph Generation](https://mlsanity.com/paper/2108.08600/) (2021)
- [Few-Shot Learning by Integrating Spatial and Frequency Representation](https://mlsanity.com/paper/2105.05348/) (2021)
- [Boosting Few-Shot Classification with View-Learnable Contrastive Learning](https://mlsanity.com/paper/2107.09242/) (2021)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition](https://mlsanity.com/paper/2105.00127/) (2021)
- [Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2207.00784/) (2022)
- [Dense Vision Transformer Compression with Few Samples](https://mlsanity.com/paper/2403.18708/) (2024)
- [Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images](https://mlsanity.com/paper/2105.11874/) (2021)
- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation](https://mlsanity.com/paper/2303.13724/) (2023)
- [GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing](https://mlsanity.com/paper/2307.14612/) (2023)
- [GeNIe: Generative Hard Negative Images Through Diffusion](https://mlsanity.com/paper/2312.02548/) (2023)
- [Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification](https://mlsanity.com/paper/2101.03526/) (2021)
- [Fine-Grained Few Shot Learning with Foreground Object Transformation](https://mlsanity.com/paper/2109.05719/) (2021)
- [Tensor feature hallucination for few-shot learning](https://mlsanity.com/paper/2106.05321/) (2021)
- [Learning to Obstruct Few-Shot Image Classification over Restricted Classes](https://mlsanity.com/paper/2409.19210/) (2024)

## Abstract (arXiv)

Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information. Mimicking the same behavior and synthesizing deformed instances of new concepts may help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images may not be visually realistic, they still maintain critical semantic information and contribute significantly to formulating classifier decision boundaries. Inspired by the recent progress of meta-learning, we combine a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner. The deformation sub-network learns to deform images by fusing a pair of images --- a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used one-shot learning benchmarks (miniImageNet and ImageNet 1K Challenge datasets), which significantly outperform state-of-the-art approaches. Code is available at https://github.com/tankche1/IDeMe-Net.