Evolving Image Compositions for Feature Representation Learning
Evolving Image Compositions for Feature Representation Learning
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发表时间：
2021-06
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通讯作者：
Paola Cascante-Bonilla;Arshdeep Sekhon;Yanjun Qi;Vicente Ordonez
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作者：
Paola Cascante-Bonilla;Arshdeep Sekhon;Yanjun Qi;Vicente Ordonez
用于视觉识别的卷积神经网络需要大量的训练样本，并且通常受益于数据增强。本文提出了一种数据增强方法PatchMix，该方法通过将成对的图像以网格状模式组合成补丁来创建新样本。这些新样本被分配的标签分数与从每个图像中借用的补丁数量成正比。然后，我们在补丁级别添加一组额外的损失来正则化并鼓励在补丁和图像级别上都有良好的表示。使用PatchMix在ImageNet上训练的ResNet-50模型在广泛的基准测试中表现出卓越的迁移学习能力。虽然PatchMix可以依靠随机配对和随机网格模式进行混合，但我们探索了进化搜索作为一种指导策略，共同发现最优的网格模式和图像配对。为此，我们设想了一个适应度函数，它绕过了重新训练模型来评估每个可能选择的需要。通过这种方式，PatchMix在CIFAR-10 (+1.91), CIFAR-100 (+5.31), Tiny Imagenet（+3.52）和Imagenet（+1.16）上优于基本模型。
Convolutional neural networks for visual recognition require large amounts of training samples and usually benefit from data augmentation. This paper proposes PatchMix, a data augmentation method that creates new samples by composing patches from pairs of images in a grid-like pattern. These new samples are assigned label scores that are proportional to the number of patches borrowed from each image. We then add a set of additional losses at the patch-level to regularize and to encourage good representations at both the patch and image levels. A ResNet-50 model trained on ImageNet using PatchMix exhibits superior transfer learning capabilities across a wide array of benchmarks. Although PatchMix can rely on random pairings and random grid-like patterns for mixing, we explore evolutionary search as a guiding strategy to jointly discover optimal grid-like patterns and image pairings. For this purpose, we conceive a fitness function that bypasses the need to re-train a model to evaluate each possible choice. In this way, PatchMix outperforms a base model on CIFAR-10 (+1.91), CIFAR-100 (+5.31), Tiny Imagenet (+3.52), and ImageNet (+1.16).