Controllable Data Generation Via Iterative Data-Property Mutual Mappings
Bo Pan Muran Qin Shiyu Wang Yifei Zhang Liang Zhao
Deep generative models have been widely used for their ability to generate
realistic data samples in various areas, such as images, molecules, text, and
speech. One major goal of data generation is controllability, namely to
generate new data with desired properties. Despite growing interest in the area
of controllable generation, significant challenges still remain, including 1)
disentangling desired properties with unrelated latent variables, 2)
out-of-distribution property control, and 3) objective optimization for
out-of-distribution property control. To address these challenges, in this
paper, we propose a general framework to enhance VAE-based data generators with
property controllability and ensure disentanglement. Our proposed objective can
be optimized on both data seen and unseen in the training set. We propose a
training procedure to train the objective in a semi-supervised manner by
iteratively conducting mutual mappings between the data and properties. The
proposed framework is implemented on four VAE-based controllable generators to
evaluate its performance on property error, disentanglement, generation
quality, and training time. The results indicate that our proposed framework
enables more precise control over the properties of generated samples in a
short training time, ensuring the disentanglement and keeping the validity of
the generated samples.