论文标题
探索具有进化策略的文本到图像生成的生成对抗网络
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies
论文作者
论文摘要
在生成模型的背景下，近年来，文本到图像生成取得了令人印象深刻的结果。提出了使用不同方法的模型，并在大量的文本和图像对数据集中进行了培训。但是，某些方法依赖于预先训练的模型，例如生成对抗网络，通过使用基于梯度的方法来更新潜在矢量的生成模型的潜在空间，并依赖于余弦功能（例如余弦功能）。在这项工作中，我们通过提出使用协方差矩阵适应演化策略来探索生成对抗网络的潜在空间，从而遵循不同的方向。我们将这种方法与使用亚当和混合策略的方法进行了比较。我们设计了一项实验研究，以使用不同的文本输入来比较三种方法，通过调整基于结果样品投影的评估方法进行图像生成，以检查分布的多样性。结果证明，进化方法在样本的产生中实现了更多的多样性，从而探索了所得网格的不同区域。此外，我们表明混合方法结合了基于梯度和进化方法的探索区域，利用结果的质量。
In the context of generative models, text-to-image generation achieved impressive results in recent years. Models using different approaches were proposed and trained in huge datasets of pairs of texts and images. However, some methods rely on pre-trained models such as Generative Adversarial Networks, searching through the latent space of the generative model by using a gradient-based approach to update the latent vector, relying on loss functions such as the cosine similarity. In this work, we follow a different direction by proposing the use of Covariance Matrix Adaptation Evolution Strategy to explore the latent space of Generative Adversarial Networks. We compare this approach to the one using Adam and a hybrid strategy. We design an experimental study to compare the three approaches using different text inputs for image generation by adapting an evaluation method based on the projection of the resulting samples into a two-dimensional grid to inspect the diversity of the distributions. The results evidence that the evolutionary method achieves more diversity in the generation of samples, exploring different regions of the resulting grids. Besides, we show that the hybrid method combines the explored areas of the gradient-based and evolutionary approaches, leveraging the quality of the results.
