Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

TL;DR

ADAMAB is an efficient, adaptive data augmentation framework that enhances few-shot implicit pattern recognition by calibrating embeddings with minimal data and computational costs, outperforming existing models.

Contribution

It introduces a novel adaptive data augmentation strategy using Multi-Armed Bandit for embedding calibration in few-shot pattern recognition tasks.

Findings

Up to 40% accuracy improvement with less than 5 samples per class

Reduces computational costs by training lightweight calibrators

Guarantees convergence in few-shot training scenarios

Abstract

Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challenging for current pre-trained foundation models such as LLMs and VLMs. While finetuning pre-trained models can improve accuracy in recognizing implicit patterns, it is usually infeasible due to a lack of training data and high computational overhead. In this paper, we propose ADAMAB, an efficient embedding calibration framework for few-shot pattern recognition. To maximally reduce the computational costs, ADAMAB trains embedder-agnostic light-weight calibrators on top of fixed embedding models without accessing their parameters. To mitigate the need for large-scale training data, we introduce an adaptive data augmentation strategy based on the Multi-Armed Bandit (MAB) mechanism. With a modified upper confidence bound algorithm, ADAMAB diminishes the gradient shifting and offers theoretically guaranteed convergence in few-shot training. Our multi-modal experiments justify the superior performance of ADAMAB, with up to 40% accuracy improvement when training with less than 5 initial data samples of each class.