  We describe a new approach that improves the training of generative
adversarial nets (GANs) for synthesizing diverse images from a text input. Our
approach is based on the conditional version of GANs and expands on previous
work leveraging an auxiliary task in the discriminator. Our generated images
are not limited to certain classes and do not suffer from mode collapse while
semantically matching the text input. A key to our training methods is how to
form positive and negative training examples with respect to the class label of
a given image. Instead of selecting random training examples, we perform
negative sampling based on the semantic distance from a positive example in the
class. We evaluate our approach using the Oxford-102 flower dataset, adopting
the inception score and multi-scale structural similarity index (MS-SSIM)
metrics to assess discriminability and diversity of the generated images. The
empirical results indicate greater diversity in the generated images,
especially when we gradually select more negative training examples closer to a
positive example in the semantic space.
