A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks
This work addresses few-shot learning for imbalanced data, offering a novel metric that improves accuracy without requiring post-processing, though it builds incrementally on existing distance-based methods.
The paper tackles the problem of imbalanced few-shot learning by proposing a maximum log-likelihood metric based on the observation that few-shot features follow exponential distributions, achieving state-of-the-art performance in both inductive and transductive settings.
Few-shot learning is a rapidly evolving area of research in machine learning where the goal is to classify unlabeled data with only one or "a few" labeled exemplary samples. Neural networks are typically trained to minimize a distance metric between labeled exemplary samples and a query set. Early few-shot approaches use an episodic training process to sub-sample the training data into few-shot batches. This training process matches the sub-sampling done on evaluation. Recently, conventional supervised training coupled with a cosine distance has achieved superior performance for few-shot. Despite the diversity of few-shot approaches over the past decade, most methods still rely on the cosine or Euclidean distance layer between the latent features of the trained network. In this work, we investigate the distributions of trained few-shot features and demonstrate that they can be roughly approximated as exponential distributions. Under this assumption of an exponential distribution, we propose a new maximum log-likelihood metric for few-shot architectures. We demonstrate that the proposed metric achieves superior performance accuracy w.r.t. conventional similarity metrics (e.g., cosine, Euclidean, etc.), and achieve state-of-the-art inductive few-shot performance. Further, additional gains can be achieved by carefully combining multiple metrics and neither of our methods require post-processing feature transformations, which are common to many algorithms. Finally, we demonstrate a novel iterative algorithm designed around our maximum log-likelihood approach that achieves state-of-the-art transductive few-shot performance when the evaluation data is imbalanced. We have made our code publicly available at https://github.com/samuelhess/MLL_FSL/.