# LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

arXiv: https://arxiv.org/abs/1905.06331 · HTML: https://mlsanity.com/paper/1905.06331/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 27 of 27)

- [Parameter Prediction for Unseen Deep Architectures](https://mlsanity.com/paper/2110.13100/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2201.04182/) (2022)
- [MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning](https://mlsanity.com/paper/2307.16424/) (2023)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms](https://mlsanity.com/paper/2103.03697/) (2021)
- [FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs](https://mlsanity.com/paper/2205.02435/) (2022)
- [Sparse Spatial Transformers for Few-Shot Learning](https://mlsanity.com/paper/2109.12932/) (2021)
- [Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition](https://mlsanity.com/paper/2207.09759/) (2022)
- [Prototype Completion for Few-Shot Learning](https://mlsanity.com/paper/2108.05010/) (2021)
- [On the Importance of Firth Bias Reduction in Few-Shot Classification](https://mlsanity.com/paper/2110.02529/) (2021)
- [Few-shot Action Recognition via Intra- and Inter-Video Information Maximization](https://mlsanity.com/paper/2305.06114/) (2023)
- [Progressive Co-Attention Network for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.08527/) (2021)
- [Adaptive Siamese Tracking with a Compact Latent Network](https://mlsanity.com/paper/2302.00930/) (2023)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI](https://mlsanity.com/paper/2312.10586/) (2023)
- [Combat Data Shift in Few-shot Learning with Knowledge Graph](https://mlsanity.com/paper/2101.11354/) (2021)
- [Meta-Ensemble Parameter Learning](https://mlsanity.com/paper/2210.01973/) (2022)
- [Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces](https://mlsanity.com/paper/2311.10051/) (2023)
- [Trainable Class Prototypes for Few-Shot Learning](https://mlsanity.com/paper/2106.10846/) (2021)
- [Generative Meta-Learning for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2305.01920/) (2023)
- [Harnessing the Power of Local Representations for Few-Shot Classification](https://mlsanity.com/paper/2407.01967/) (2024)
- [TLRM: Task-level Relation Module for GNN-based Few-Shot Learning](https://mlsanity.com/paper/2101.09840/) (2021)
- [A Transductive Maximum Margin Classifier for Few-Shot Learning](https://mlsanity.com/paper/2107.11975/) (2021)
- [Exploring intra-task relations to improve meta-learning algorithms](https://mlsanity.com/paper/2312.16612/) (2023)
- [Memory-Reduced Meta-Learning with Guaranteed Convergence](https://mlsanity.com/paper/2412.12030/) (2024)

## Abstract (arXiv)

In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters for similar unseen tasks with training samples. Our approach, called LGM-Net, includes two key modules, namely, TargetNet and MetaNet. The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples. We also present an intertask normalization strategy for the training process to leverage common information shared across different tasks. The experimental results on Omniglot and miniImageNet datasets demonstrate that LGM-Net can effectively adapt to similar unseen tasks and achieve competitive performance, and the results on synthetic datasets show that transferable prior knowledge is learned by the MetaNet module via mapping training data to functional weights. LGM-Net enables fast learning and adaptation since no further tuning steps are required compared to other meta-learning approaches.