论文标题
软化图像分类
Soft Augmentation for Image Classification
论文作者
论文摘要
现代神经网络被过度参数化，因此依赖于强大的正则化，例如数据增强和重量衰减，以减少过度拟合并改善概括。数据增强的主要形式应用了不变的变换，其中样本的学习目标是应用于该样品的转换的不变。我们从人类的视觉分类研究中汲取灵感，并提出通过不变转换的概括增强，以软增强，其中学习目标随着应用于样本的转换程度的函数而非线性柔软：例如，更具侵略性的图像作物增强产生了较少自信的学习目标。我们证明，软目标允许更具积极的数据扩大，提供更强大的性能提高，使用其他增强政策，有趣的是，可以产生更好的校准模型（因为培训它们对积极的裁剪/封闭示例的积极信心较低）。结合现有的积极增强策略，软目标1）将CIFAR-10，CIFAR-100，Imagenet-1K和Imagenet-V2的Top-1精度提高提高一倍，将模型闭塞性能提高到最多$ 4 \ times $，而3）将预期的校准误差（ECE）提高了一半。最后，我们表明，软增强概括为自我监督的分类任务。可在https://github.com/youngleox/soft_augmentation上找到代码
Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample. We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e.g., more aggressive image crop augmentations produce less confident learning targets. We demonstrate that soft targets allow for more aggressive data augmentation, offer more robust performance boosts, work with other augmentation policies, and interestingly, produce better calibrated models (since they are trained to be less confident on aggressively cropped/occluded examples). Combined with existing aggressive augmentation strategies, soft target 1) doubles the top-1 accuracy boost across Cifar-10, Cifar-100, ImageNet-1K, and ImageNet-V2, 2) improves model occlusion performance by up to $4\times$, and 3) halves the expected calibration error (ECE). Finally, we show that soft augmentation generalizes to self-supervised classification tasks. Code available at https://github.com/youngleox/soft_augmentation
