面向小规模数据集的学习状态感知动态生成式数据增强

中文摘要 AI 辅助

小规模图像分类常受训练数据稀缺性限制，基于预训练生成模型的生成式数据增强（GDA）已成为有效解决方案。然而，现有方法依赖与任务无关的增强策略，忽略下游模型需求。尽管近期动态GDA方法结合模型反馈指导增强，但仍难以可靠确定样本特定的增强强度，也无法在平衡图像多样性与类别语义的同时，使增强策略适配不同图像区域。为解决这些问题，我们提出学习状态感知动态生成式数据增强（LSADA）。具体而言，LSADA基于每个样本的当前损失及损失下降率构建其学习状态，再将该状态映射为样本特定的增强强度。此外，LSADA引入解耦数据增强与扩散融合策略，对类别相关区域应用强度可控的变换，对类别无关区域生成多样化内容，逐步融合以提升图像多样性并保留类别语义。在9个公开数据集上的实验表明，LSADA在6个自然图像数据集上较现有SOTA动态GDA方法平均提升4.5%，在3个医学图像数据集上平均提升2.5%。

英文摘要

Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effective solution. However, existing methods rely on task-agnostic augmentation strategies that overlook downstream model needs. Although recent dynamic GDA methods incorporate model feedback to guide augmentation, they still struggle to reliably determine sample-specific augmentation strengths and adapt augmentation strategies to different image regions while balancing image diversity and class semantics. To address these issues, we propose learning-state-aware dynamic generative data augmentation (LSADA). Specifically, LSADA constructs a learning state for each sample based on its current loss and loss-decrease rate, which is then mapped to a sample-specific augmentation strength. Furthermore, LSADA introduces a decoupled data augmentation and diffusion fusion strategy that applies strength-controlled transformations to class-relevant regions and generates diverse class-irrelevant regions, progressively fusing them to improve image diversity while preserving class semantics. Experiments on nine public datasets show that LSADA outperforms the existing SOTA dynamic GDA method by an average of 4.5% on six natural image datasets and 2.5% on three medical image datasets.

发表机构

• Hunan University（湖南大学）

机构由 AI 辅助整理，请以论文原文为准。