  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.
