论文标题
多类非对抗图像合成，并应用于非常小样本的分类
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
论文作者
论文摘要
当前，合成图像的产生由生成对抗网络（GAN）主导。尽管它们在产生逼真的图像方面取得了杰出的成功，但他们仍然遭受重大缺点，包括不稳定且高度敏感的训练程序，模式崩溃和模式混合以及对大型训练集的依赖。在这项工作中，我们提出了一种新型的非对抗性生成方法 - 潜在空间的聚类优化（COLA），它克服了gan的某些局限性，并且在培训数据稀缺时胜过gan。在完整的数据制度中，我们的方法能够在没有监督的情况下生成多样化的多级图像，从而超过了以前的非对抗方法，从图像质量和多样性方面。在小型数据制度中，只有一小部分标记的图像可以用于培训而无法访问其他未标记的数据，我们的结果超过了接受相同数量数据的最先进的GAN模型。最后，当利用我们的模型增强小型数据集时，我们在挑战数据集（包括CIFAR-10，CIFAR-100，STL-10，STL-10和Tiny-Imagenet）上超越了小样本分类任务中的最新性能。提出了支持该方法本质的理论分析。
The generation of synthetic images is currently being dominated by Generative Adversarial Networks (GANs). Despite their outstanding success in generating realistic looking images, they still suffer from major drawbacks, including an unstable and highly sensitive training procedure, mode-collapse and mode-mixture, and dependency on large training sets. In this work we present a novel non-adversarial generative method - Clustered Optimization of LAtent space (COLA), which overcomes some of the limitations of GANs, and outperforms GANs when training data is scarce. In the full data regime, our method is capable of generating diverse multi-class images with no supervision, surpassing previous non-adversarial methods in terms of image quality and diversity. In the small-data regime, where only a small sample of labeled images is available for training with no access to additional unlabeled data, our results surpass state-of-the-art GAN models trained on the same amount of data. Finally, when utilizing our model to augment small datasets, we surpass the state-of-the-art performance in small-sample classification tasks on challenging datasets, including CIFAR-10, CIFAR-100, STL-10 and Tiny-ImageNet. A theoretical analysis supporting the essence of the method is presented.
