This work addresses fair generative models. Dataset biases have been a major
cause of unfairness in deep generative models. Previous work had proposed to
augment large, biased datasets with small, unbiased reference datasets. Under
this setup, a weakly-supervised approach has been proposed, which achieves
state-of-the-art quality and fairness in generated samples. In our work, based
on this setup, we propose a simple yet effective approach. Specifically, first,
we propose fairTL, a transfer learning approach to learn fair generative
models. Under fairTL, we pre-train the generative model with the available
large, biased datasets and subsequently adapt the model using the small,
unbiased reference dataset. We find that our fairTL can learn expressive sample
generation during pre-training, thanks to the large (biased) dataset. This
knowledge is then transferred to the target model during adaptation, which also
learns to capture the underlying fair distribution of the small reference
dataset. Second, we propose fairTL++, where we introduce two additional
innovations to improve upon fairTL: (i) multiple feedback and (ii)
Linear-Probing followed by Fine-Tuning (LP-FT). Taking one step further, we
consider an alternative, challenging setup when only a pre-trained (potentially
biased) model is available but the dataset that was used to pre-train the model
is inaccessible. We demonstrate that our proposed fairTL and fairTL++ remain
very effective under this setup. We note that previous work requires access to
the large, biased datasets and is incapable of handling this more challenging
setup. Extensive experiments show that fairTL and fairTL++ achieve
state-of-the-art in both quality and fairness of generated samples. The code
and additional resources can be found at bearwithchris.github.io/fairTL/.

该研究提出了一种基于转移学习的公平生成模型方法，利用大规模含偏见、小规模不含偏见的数据集作为训练数据，在不同的模型中进行知识迁移，实现生成样本的高质量和公正性。