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LearningToCompare_FSL is a PyTorch implementation of the “Learning to Compare: Relation Network for Few-Shot Learning” paper, focusing on the few-shot learning experiments described in that work. The core idea implemented here is the relation network, which learns to compare pairs of feature embeddings and output relation scores that indicate whether two images belong to the same class, enabling classification from only a handful of labeled examples. The repository provides training and evaluation code for standard few-shot benchmarks such as miniImageNet and Omniglot, making it possible to reproduce the experimental results reported in the paper. It includes model definitions, data loading logic, episodic training loops, and scripts that implement the N-way K-shot evaluation protocol common in few-shot research. Researchers can use this codebase as a starting point to test new ideas, modify relation modules, or transfer the approach to new datasets.
Features
- PyTorch implementation of relation networks for few-shot learning
- Ready-to-run training and evaluation on benchmarks like miniImageNet and Omniglot
- Episodic N-way K-shot training pipeline for meta-learning experiments
- Modular model and data loading code that can be adapted to new datasets
- Reproducible baseline for the “Learning to Compare” few-shot classification approach
- Open-source reference for researchers exploring metric-based and relation-based few-shot methods
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