Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Metric-based meta-learning is effective for few-shot image classification by analyzing sample relationships in embedding spaces.
• Overfitting is a challenge in few-shot learning due to limited training samples.
• Generalization of embedding spaces remains a critical hurdle for metric-based meta-learning.

Purpose of the Study:

• To propose a novel feature learning method, Instance Neighbor Constraints (INC), to improve metric-based meta-learning.
• To enhance the optimization of metric-based models by integrating INC into an alternate optimization training (AOT) framework.
• To demonstrate the effectiveness of the proposed method in improving few-shot image classification performance.

Main Methods:

• Leveraging Instance Neighbor Constraints (INC) to learn feature representations that preserve sample neighbor relationships.
• Integrating INC into an alternate optimization training (AOT) framework combining batch and episode learning.
• Extensive experimentation on miniImageNet, tieredImageNet, FC100, and CUB datasets in 5-way 1-shot and 5-way 5-shot settings.

Main Results:

• The proposed INC method demonstrates significant improvements in learning efficiency and overall model performance.
• The integrated AOT framework effectively optimizes metric-based models, leading to better generalization.
• Consistent performance gains were observed across multiple few-shot image benchmarks, achieving state-of-the-art results.

Conclusions:

• Appropriately initializing the embedding space using methods like INC is crucial for preventing suboptimal solutions in metric-based meta-learning.
• Combining batch and episode learning within an alternate optimization framework further boosts few-shot learning capabilities.
• The proposed approach offers a promising direction for advancing few-shot image classification research.