论文标题
弹性重量合并的几声图像产生
Few-shot Image Generation with Elastic Weight Consolidation
论文作者
论文摘要
很少有图像生成旨在生成给定域的更多数据，只有很少的可用培训示例。由于期望完全从几个观察结果（例如表情符号）完全推断出分布是不合理的，因此我们试图利用一个较大的相关来源领域作为预处理（例如，人的面孔）。因此，我们希望在适应目标外观的同时保留源域的多样性。我们在不引入任何其他参数的情况下调整了一个验证的模型，以适用于目标域的几个示例。至关重要的是，我们在适应过程中将权重的更改正常，以便在适合目标的同时最好地保留源数据集的信息。我们通过产生不同目标域的高质量结果（包括极少数示例的算法）来证明我们的算法的有效性（例如，<10）。我们还分析了我们的方法的性能相对于一些重要因素，例如示例数量以及源和目标域之间的差异。
Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution from just a few observations (e.g., emojis), we seek to leverage a large, related source domain as pretraining (e.g., human faces). Thus, we wish to preserve the diversity of the source domain, while adapting to the appearance of the target. We adapt a pretrained model, without introducing any additional parameters, to the few examples of the target domain. Crucially, we regularize the changes of the weights during this adaptation, in order to best preserve the information of the source dataset, while fitting the target. We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., <10). We also analyze the performance of our method with respect to some important factors, such as the number of examples and the dissimilarity between the source and target domain.
