Generalization Bounds for Unsupervised Image to Image translations with WGANs. by Tomer Galanti (tau)

Mar. 17th 2019, Sun. 12:00 , Tomer Galanti (webpage).

Tel-Aviv University (PhD Student).

Location: Gonda Building (901), Room 101.

Generalization Bounds for Unsupervised Image to Image translations with WGANs.

Abstract:

The recent empirical success of cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications. This lacuna is especially troubling, given the clear ambiguity in such mappings.

We work with the adversarial training method called the Wasserstein GAN and derive a novel generalization bound, which limits the risk between the learned mapping $h$ and the target mapping $y$, by a sum of two terms: (i) the risk between $h$ and the most distant alternative mapping that was learned by the same cross-domain mapping algorithm, and (ii) the minimal Wasserstein GAN divergence between the target domain and the domain obtained by applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is a hypothesis selected by the same algorithm. The bound is directly related to Occam’s razor and it encourages the selection of the minimal architecture that supports a small Wasserstein GAN divergence.

The bound leads to multiple algorithmic consequences, including a method for hyperparameter selection and for an early stopping in cross-domain mapping GANs. We also demonstrate a novel capability for unsupervised learning of estimating confidence in the mapping of every specific sample. Lastly, we show how non-minimal architectures can be effectively trained by an inverted knowledge distillation in which a minimal architecture is used to train a larger one, leading to higher quality outputs.

Bio: A PhD student at Tel Aviv University, under the supervision of Prof. Lior Wolf, with a focus on the theoretical aspects of unsupervised learning and deep learning.