  Data-efficient image classification using deep neural networks in settings,
where only small amounts of labeled data are available, has been an active
research area in the recent past. However, an objective comparison between
published methods is difficult, since existing works use different datasets for
evaluation and often compare against untuned baselines with default
hyper-parameters. We design a benchmark for data-efficient image classification
consisting of six diverse datasets spanning various domains (e.g., natural
images, medical imagery, satellite data) and data types (RGB, grayscale,
multispectral). Using this benchmark, we re-evaluate the standard cross-entropy
baseline and eight methods for data-efficient deep learning published between
2017 and 2021 at renowned venues. For a fair and realistic comparison, we
carefully tune the hyper-parameters of all methods on each dataset.
Surprisingly, we find that tuning learning rate, weight decay, and batch size
on a separate validation split results in a highly competitive baseline, which
outperforms all but one specialized method and performs competitively to the
remaining one.
