ROBUST ADVERSARIAL LEARNING FOR SEMI-SUPERVISED SEMANTIC SEGMENTATION

The semi-supervised semantic segmentation adversarial learning well reduces the use of a large number of manually labeled labels. However, the convolution operator of the generator in the generative adversarial network (GAN) has a local receptive field, so it can only deal with long-range dependencies after passing through multiple convolutional layers. In order to solve this problem, we added two layers of self-attention modules to the GAN generator, and modeled the semantic dependency relationship in the spatial dimension. The self-attention module selectively aggregates the features at each location by weighting and summing the features at all locations. Some recent studies have shown that the adjustment of the discriminator affects the performance of GAN. In order to solve the problem of GAN training instability, we applied spectral normalization to the GAN discriminator and found that this improved the stability of the training. Our method has better performance than existing full/semi-supervised semantic image segmentation techniques.