  Domain alignment is currently the most prevalent solution to unsupervised
domain-adaptation tasks and are often being presented as minimizers of some
theoretical upper-bounds on risk in the target domain. However, further works
revealed severe inadequacies between theory and practice: we consolidate this
analysis and confirm that imposing domain invariance on features is neither
necessary nor sufficient to obtain low target risk. We instead argue that
successful deep domain adaptation rely largely on hidden inductive biases found
in the common practice, such as model pre-training or design of encoder
architecture. We perform various ablation experiments on popular benchmarks and
our own synthetic transfers to illustrate their role in prototypical
situations. To conclude our analysis, we propose to meta-learn parametric
inductive biases to solve specific transfers and show their superior
performance over handcrafted heuristics.
