论文标题
利用潜在代码：交互式时尚产品生成，相似的图像检索和跨类别建议使用变分自动编码器
Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders
论文作者
论文摘要
时装行业深度学习应用的兴起促进了策划大规模数据集的进步，以构建产品设计，图像检索和推荐系统的应用程序。在本文中，作者建议使用变分自动编码器（VAE）构建一个交互式时尚产品应用程序框架，该框架使用户可以根据自己的喜好生成具有属性的产品，为同一产品类别检索类似样式，并从其他类别中接收基于内容的建议。时尚产品图像数据集包含眼镜，鞋类和袋子适合说明该管道适用于电子商务的蓬勃发展行业，从而使直接用户互动可以指定所需的产品以及新的数据匹配方法，并通过使用VAE来使用VAE并利用其生成的潜在代码。
The rise of deep learning applications in the fashion industry has fueled advances in curating large-scale datasets to build applications for product design, image retrieval, and recommender systems. In this paper, the author proposes using Variational Autoencoder (VAE) to build an interactive fashion product application framework that allows the users to generate products with attributes according to their liking, retrieve similar styles for the same product category, and receive content-based recommendations from other categories. Fashion product images dataset containing eyewear, footwear, and bags are appropriate to illustrate that this pipeline is applicable in the booming industry of e-commerce enabling direct user interaction in specifying desired products paired with new methods for data matching, and recommendation systems by using VAE and exploiting its generated latent codes.
