  Efficient transfer learning (ETL) is receiving increasing attention to adapt
large pre-trained language-vision models on downstream tasks with a few labeled
samples. While significant progress has been made, we reveal that
state-of-the-art ETL approaches exhibit strong performance only in
narrowly-defined experimental setups, and with a careful adjustment of
hyperparameters based on a large corpus of labeled samples. In particular, we
make two interesting, and surprising empirical observations. First, to
outperform a simple Linear Probing baseline, these methods require to optimize
their hyper-parameters on each target task. And second, they typically
underperform -- sometimes dramatically -- standard zero-shot predictions in the
presence of distributional drifts. Motivated by the unrealistic assumptions
made in the existing literature, i.e., access to a large validation set and
case-specific grid-search for optimal hyperparameters, we propose a novel
approach that meets the requirements of real-world scenarios. More concretely,
we introduce a CLass-Adaptive linear Probe (CLAP) objective, whose balancing
term is optimized via an adaptation of the general Augmented Lagrangian method
tailored to this context. We comprehensively evaluate CLAP on a broad span of
datasets and scenarios, demonstrating that it consistently outperforms SoTA
approaches, while yet being a much more efficient alternative.
