通过多图混合生成新图像，提升小数据集图像分类效果
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
- 用分割掩码在特征空间混合同类别多张图片
- 在ciFAIR-10等小数据集上超越现有最佳方法
- 适合标注数据少的现实场景应用
训练深度神经网络需要大量带标注的数据集，但数据收集与标注成本高昂，且存在法律和隐私问题，严重限制了实际应用。为此，我们提出HydraMix，一种新架构，通过混合同一类别的多张图片生成新图像。HydraMix在特征空间中利用基于分割的混合掩码引导内容融合，并通过无监督与对抗性训练进行优化。该数据增强方案使模型可从极小数据集从头训练。我们在ciFAIR-10、STL-10和ciFAIR-100上进行了广泛实验，并引入新的图文评估指标以衡量增强数据集的泛化能力。结果表明，HydraMix在小数据集图像分类任务上优于现有最先进方法。
原文摘要 · Abstract (English)
Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also faces legal and privacy problems. These factors are a significant limitation for many real-world applications. To address this, we introduce HydraMix, a novel architecture that generates new image compositions by mixing multiple different images from the same class. HydraMix learns the fusion of the content of various images guided by a segmentation-based mixing mask in feature space and is optimized via a combination of unsupervised and adversarial training. Our data augmentation scheme allows the creation of models trained from scratch on very small datasets. We conduct extensive experiments on ciFAIR-10, STL-10, and ciFAIR-100. Additionally, we introduce a novel text-image metric to assess the generality of the augmented datasets. Our results show that HydraMix outperforms existing state-of-the-art methods for image classification on small datasets.
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