Synthetic to real adaptation with generative correlation alignment network

3 contributions:

• We propose a Deep Generative Correlation Alignment Network(DGCAN) to synthesize CAD objects contour from the CAD-synthetic domain with natural textures from the real image domain.
• We explore the effect of applying the content and CORAL losses on different layers and determine the optimal configuration to generate the most promising stimuli.
• We empirically show the effectiveness of our model over several state-of-the-art methods by testing on real image datasets.

Shape Preserving loss:

Naturalness loss: