Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasp ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.