论文标题
通过元学习加强生成的图像，以进行一次性细粒的视觉识别
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
论文作者
论文摘要
一声细粒度的视觉识别通常会遇到很少的培训示例的新细粒类别的问题。为了减轻这个问题，基于生成对抗网络（GAN）的现成图像生成技术可能会创建其他训练图像。但是，这些GAN生成的图像通常无助于实际提高单次细粒识别的准确性。在本文中，我们提出了一个元学习框架，以将生成的图像与原始图像相结合，以便由此产生的“混合”训练图像可以改善单一学习。具体而言，通用图像发生器通过一些新型类的培训实例进行了更新，并提出了元图像增强网络（METAIRNET）来进行一次性细粒度识别以及图像增强。我们的实验表明，在单发细分图像分类基准上，基准对基准的一致性一致。此外，我们的分析表明，与原始图像相比，增强图像具有更多的多样性。
One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we propose a meta-learning framework to combine generated images with original images, so that the resulting "hybrid" training images improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.
