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:

Synthetic to real adaptation with generative correlation alignment network

Naturalness loss:

Synthetic to real adaptation with generative correlation alignment network