论文标题
信心指导的数据增强，以改善半监督培训
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training
论文作者
论文摘要
我们提出了一种新的策略，以提高图像分类的准确性和鲁棒性。首先，我们训练基线CNN模型。然后，我们通过识别所有错误分类的样本以及正确分类的置信值值来确定特征空间中的具有挑战性的区域。然后，这些样品用于训练变异自动编码器（VAE）。接下来，VAE用于生成合成图像。最后，生成的合成图像与原始标记的图像结合使用，以半监督的方式训练新模型。基准数据集（例如STL10和CIFAR-100）上的经验结果表明，合成生成的样品可以进一步多样化训练数据，从而与仅使用可用数据的完全监督的基线方法相比，可以改善图像分类。
We propose a new strategy to improve the accuracy and robustness of image classification. First, we train a baseline CNN model. Then, we identify challenging regions in the feature space by identifying all misclassified samples, and correctly classified samples with low confidence values. These samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE is used to generate synthetic images. Finally, the generated synthetic images are used in conjunction with the original labeled images to train a new model in a semi-supervised fashion. Empirical results on benchmark datasets such as STL10 and CIFAR-100 show that the synthetically generated samples can further diversify the training data, leading to improvement in image classification in comparison with the fully supervised baseline approaches using only the available data.
