  This article proposes a novel approach for augmenting generative adversarial
network (GAN) with a self-supervised task in order to improve its ability for
encoding video representations that are useful in downstream tasks such as
human activity recognition. In the proposed method, input video frames are
randomly transformed by different spatial transformations, such as rotation,
translation and shearing or temporal transformations such as shuffling temporal
order of frames. Then discriminator is encouraged to predict the applied
transformation by introducing an auxiliary loss. Subsequently, results prove
superiority of the proposed method over baseline methods for providing a useful
representation of videos used in human activity recognition performed on
datasets such as KTH, UCF101 and Ball-Drop. Ball-Drop dataset is a specifically
designed dataset for measuring executive functions in children through
physically and cognitively demanding tasks. Using features from proposed method
instead of baseline methods caused the top-1 classification accuracy to
increase by more then 4%. Moreover, ablation study was performed to investigate
the contribution of different transformations on downstream task.
