47 research outputs found
Disentanglement by Cyclic Reconstruction
Deep neural networks have demonstrated their ability to automatically extract
meaningful features from data. However, in supervised learning, information
specific to the dataset used for training, but irrelevant to the task at hand,
may remain encoded in the extracted representations. This remaining information
introduces a domain-specific bias, weakening the generalization performance. In
this work, we propose splitting the information into a task-related
representation and its complementary context representation. We propose an
original method, combining adversarial feature predictors and cyclic
reconstruction, to disentangle these two representations in the single-domain
supervised case. We then adapt this method to the unsupervised domain
adaptation problem, consisting of training a model capable of performing on
both a source and a target domain. In particular, our method promotes
disentanglement in the target domain, despite the absence of training labels.
This enables the isolation of task-specific information from both domains and a
projection into a common representation. The task-specific representation
allows efficient transfer of knowledge acquired from the source domain to the
target domain. In the single-domain case, we demonstrate the quality of our
representations on information retrieval tasks and the generalization benefits
induced by sharpened task-specific representations. We then validate the
proposed method on several classical domain adaptation benchmarks and
illustrate the benefits of disentanglement for domain adaptation
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial
networks, borrowing from style transfer literature. The new architecture leads
to an automatically learned, unsupervised separation of high-level attributes
(e.g., pose and identity when trained on human faces) and stochastic variation
in the generated images (e.g., freckles, hair), and it enables intuitive,
scale-specific control of the synthesis. The new generator improves the
state-of-the-art in terms of traditional distribution quality metrics, leads to
demonstrably better interpolation properties, and also better disentangles the
latent factors of variation. To quantify interpolation quality and
disentanglement, we propose two new, automated methods that are applicable to
any generator architecture. Finally, we introduce a new, highly varied and
high-quality dataset of human faces.Comment: CVPR 2019 final versio