41 research outputs found
On the Fairness of Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are one of the greatest advances in AI
in recent years. With their ability to directly learn the probability
distribution of data, and then sample synthetic realistic data. Many
applications have emerged, using GANs to solve classical problems in machine
learning, such as data augmentation, class unbalance problems, and fair
representation learning. In this paper, we analyze and highlight fairness
concerns of GANs model. In this regard, we show empirically that GANs models
may inherently prefer certain groups during the training process and therefore
they're not able to homogeneously generate data from different groups during
the testing phase. Furthermore, we propose solutions to solve this issue by
conditioning the GAN model towards samples' group or using ensemble method
(boosting) to allow the GAN model to leverage distributed structure of data
during the training phase and generate groups at equal rate during the testing
phase.Comment: submitted to International Joint Conference on Neural Networks
(IJCNN) 202
Pose-Normalized Image Generation for Person Re-identification
Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for synthesizing realistic person images conditional on the
pose. The model is based on a generative adversarial network (GAN) designed
specifically for pose normalization in re-id, thus termed pose-normalization
GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
re-id feature free of the influence of pose variations. We show that this
feature is strong on its own and complementary to features learned with the
original images. Importantly, under the transfer learning setting, we show that
our model generalizes well to any new re-id dataset without the need for
collecting any training data for model fine-tuning. The model thus has the
potential to make re-id model truly scalable.Comment: 10 pages, 5 figure