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Recently, convolutional neural networks (CNNs) have achieved state-of-the-art performance in infrared small target detection. However, the limited number of public training data restricts the performance improvement of CNN-based methods. To handle the scarcity of training data, we propose a method that can generate synthetic training data for infrared small target detection. We adopt the generative adversarial network framework where synthetic background images and infrared small targets are generated in two independent processes. In the first stage, we synthesize infrared images by transforming visible images into infrared ones. In the second stage, target masks are implanted on the transformed images. Then, the proposed intensity modulation network synthesizes realistic target objects that can be diversely generated from further image processing. Experimental results on the recent public dataset show that, when we train various detection networks using the dataset composed of both real and synthetic images, detection networks yield better performance than using real data only.