Performance of single-task learning versus meta-learning as a function of the support set size When using support sets composed of just 16 randomly selected training samples (i.e., labeled molecules) for each of the 157 test tasks of the FS-Mol benchmark set, the prototypical network outperformed the random forest in 140 of the tasks (average ROC-AUC 0.70 vs. 0.58, representing a plus of 0.120; Figure ). As the size of the support sets was increased, the advantage of the prototypical network over the random forest diminished. Measured as ΔROC-AUC, the benefit of the meta-learning approach over the single-task approach decreased from 0.120 for (and ) training samples to +0.092 for . These 𝑛 𝑡𝑟𝑎𝑖𝑛 = 16 𝑛 𝑡𝑟𝑎𝑖𝑛 = 32 𝑛 𝑡𝑟𝑎𝑖𝑛 = 128 results confirm that meta-learning yields better results in low-data regimes.