Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines.

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using textdescriptions orreference images, while preserving irrelevant attributes(e.g.,identity,background,cloth).

Thegoal targetobjectisinthefridge. 4000 steps 3. Set Table: Getabowl (see Fig.19c).

In this work, we propose a novel and surprisingly simple routing strategy called self-routing, where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore, but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR10, SVHN, and SmallNORB showthat the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.

Exploiting the short-sighted gradients should be balanced with adequate explorations.

Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches?

In this paper we consider the case of generativemodels. In particular,we investigate generativeadversarialnetworks (GANs) in the task of learning new categories in a sequential fashion.

See Corollary 2 in Appendixfordetail.

Machine comprehension (MC) has gained significant popularity over the past few years and it is a coveted goal in the field of natural language understanding. Its task is to teach the machine to understand thecontent ofagivenpassage andthenanswer arelated question, which requires deep comprehension and accurate information extraction towards the text.

Influence functions estimate the effect of removing a training point on a model without theneedtoretrain. Theyarebasedonafirst-order Taylorapproximation thatisguaranteed tobeaccurate forsufficiently small changes tothemodel, and so are commonly used to study the effect of individual points in large datasets. However, we often want to study the effects of largegroups of training points, e.g., todiagnose batch effects orapportion credit between different data sources.