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.

本论文提出一种基于生成对抗网络的动作识别框架，使用大规模视频活动数据集上的深度卷积生成对抗网络（DCGAN）进行无监督学习，然后通过有标签数据集对已训练的鉴别器进行微调，实现准确识别人类活动的半监督学习。其中本文找到了好的网络架构和超参数以最大化鉴别器的效果。这种只使用外观信息的半监督学习方法达到了目前半监督动作识别方法，如 UCF101 和 HMDB51 等，的优劣表现。