论文标题
通过多任务学习，有效的图像库表示
Efficient Image Gallery Representations at Scale Through Multi-Task Learning
论文作者
论文摘要
图像库提供了有关产品的丰富信息来源，这些信息可以在许多建议和检索应用中利用。我们研究了通过多任务学习（MTL）方法来构建通用图像库编码器的问题，并证明这确实是实现对新下游任务的学习表征的普遍性的实际方法。此外，我们分析了MTL训练的解决方案对最佳且基本更昂贵的解决方案的相对预测性能，并发现MTL可能是解决低资源二进制任务中稀疏性的有用机制。
Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.
