用强化学习优化数据增强，少样本下显著提升隐式模式识别准确率
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition
- 基于多臂老虎机自适应选择增强策略，减少无效数据生成
- 仅用每类少于5个样本，最高提升40%识别准确率
- 无需修改主模型，轻量级校准器适合资源受限场景
识别隐式视觉与文本模式在现代人工智能的诸多实际应用中至关重要。然而，当前预训练基础模型（如LLMs和VLMs）在处理长尾模式识别任务时仍面临挑战。尽管微调可提升识别精度，但因训练数据稀缺且计算开销高而难以实施。本文提出ADAMAB，一种高效的嵌入校准框架，用于少样本模式识别。为最大限度降低计算成本，ADAMAB在固定嵌入模型上训练无嵌入器依赖的轻量级校准器，不访问其参数。为缓解对大规模训练数据的需求，引入基于多臂老虎机（MAB）机制的自适应数据增强策略。通过改进的上置信界算法，ADAMAB有效抑制梯度漂移，并在少样本训练中实现理论保证的收敛性。多模态实验验证了其优越性能，在每类初始样本少于5个时，最高实现40%的准确率提升。
原文摘要 · Abstract (English)
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.
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