Multi-domain learning (MDL) aims at obtaining a model with minimal average
risk across multiple domains. Our empirical motivation is automated microscopy
data, where cultured cells are imaged after being exposed to known and unknown
chemical perturbations, and each dataset displays significant experimental
bias. This paper presents a multi-domain adversarial learning approach, MuLANN,
to leverage multiple datasets with overlapping but distinct class sets, in a
semi-supervised setting. Our contributions include: i) a bound on the average-
and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss
to accommodate semi-supervised multi-domain learning and domain adaptation;
iii) the experimental validation of the approach, improving on the state of the
art on two standard image benchmarks, and a novel bioimage dataset, Cell.

本文介绍了一种名为 MuLANN 的对抗性的多域学习方法，用于利用多个具有重叠但不同类集的数据集，在半监督设置中进行多域学习和域自适应，并通过实验验证其有效性，提高了在两个标准图像基准测试和一个新的生物图像数据集 Cell 上的表现。该方法的贡献包括：利用 H 散度得到的 MDL 中平均和最差域风险的界限，适用于半监督的多域学习和域自适应的新损失。