Fig. 2

Overview of GAN-DL self-supervised representation learning framework, whose pretext task consists in the adversarial game between the generator and the discriminator of the backbone StyleGAN2 (a). The discriminator’s features are exploited to several downstream tasks (b): (1) Controls classification - classification of active and inactive compounds against SARS-CoV2 in two different cell models; (2) Dose-response modelling—disease-associated profiling from raw microscopy images; (3) Cell models classification—zero-shot representation learning classification task consisting in categorizing four different cell types