  We propose an action recognition framework using Gen- erative Adversarial
Networks. Our model involves train- ing a deep convolutional generative
adversarial network (DCGAN) using a large video activity dataset without la-
bel information. Then we use the trained discriminator from the GAN model as an
unsupervised pre-training step and fine-tune the trained discriminator model on
a labeled dataset to recognize human activities. We determine good network
architectural and hyperparameter settings for us- ing the discriminator from
DCGAN as a trained model to learn useful representations for action
recognition. Our semi-supervised framework using only appearance infor- mation
achieves superior or comparable performance to the current state-of-the-art
semi-supervised action recog- nition methods on two challenging video activity
datasets: UCF101 and HMDB51.
