Hyperbolic Insights With Knowledge Distillation for Cross-Domain Few-Shot Learning

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Cross-domain few-shot learning (CDFL) seeks rapid generalization from limited data across different domains.
• Existing methods struggle with domain disparities and high-dimensional Euclidean spaces for generalized feature embeddings.
• Current reliance on Euclidean metric classifiers limits performance due to inherent data domain differences.

Purpose of the Study:

• To introduce a novel CDFL method, Hyperbolic Insights with Knowledge Distillation (HIKD), to overcome limitations of Euclidean-based approaches.
• To enhance model generalization and task performance through knowledge distillation and hyperbolic space utilization.
• To address inter-domain gaps by adapting embedded features in the meta-testing phase.

Main Methods:

• Mapping Euclidean features to hyperbolic space using hyperbolic embedding.
• Employing a hyperbolic fitting distillation method during meta-training for unified domain representation.
• Utilizing a hyperbolic adaptive module in meta-testing to mitigate source-target domain biases.

Main Results:

• HIKD demonstrates superior performance compared to state-of-the-art methods on the Meta-Dataset.
• Achieved an average accuracy of 80.6%, indicating significant improvements in cross-domain generalization.
• Hyperbolic space effectively captures hierarchical structures and larger data capacities, aiding generalized learning.

Conclusions:

• HIKD effectively addresses challenges in cross-domain few-shot learning by leveraging hyperbolic geometry and knowledge distillation.
• The proposed method shows strong potential for improving generalization capabilities in scenarios with limited labeled data across domains.
• Hyperbolic embedding and adaptive modules are crucial for bridging domain gaps and enhancing model robustness.