  Deep Ensembles (DE) are a prominent approach for achieving excellent
performance on key metrics such as accuracy, calibration, uncertainty
estimation, and out-of-distribution detection. However, hardware limitations of
real-world systems constrain to smaller ensembles and lower-capacity networks,
significantly deteriorating their performance and properties. We introduce
Packed-Ensembles (PE), a strategy to design and train lightweight structured
ensembles by carefully modulating the dimension of their encoding space. We
leverage grouped convolutions to parallelize the ensemble into a single shared
backbone and forward pass to improve training and inference speeds. PE is
designed to operate within the memory limits of a standard neural network. Our
extensive research indicates that PE accurately preserves the properties of DE,
such as diversity, and performs equally well in terms of accuracy, calibration,
out-of-distribution detection, and robustness to distribution shift. We make
our code available at https://github.com/ENSTA-U2IS/torch-uncertainty.
