Abstract We propose GAN-Supervised Learning, a framework for learning discriminative
models and their GAN-generated training data jointly end-to-end. We apply our framework to …

Large intra-class variation is the result of changes in multiple object characteristics. Images,
however, only show the superposition of different variable factors such as appearance or …

We present MixNMatch, a conditional generative model that learns to disentangle and
encode background, object pose, shape, and texture from real images with minimal …

Robot target recognition is a critical and fundamental machine vision task. In this paper,
InVision, a robot target recognition approach is proposed using deep federated learning …

Disentangled generative models map a latent code vector to a target space, while enforcing
that a subset of the learned latent codes are interpretable and associated with distinct …

We introduce a new method for diverse foreground generation with explicit control over
various factors. Existing image inpainting based foreground generation methods often …

Disentangled representation learning finds compact, independent and easy-to-interpret
factors of the data. Learning such has been shown to require an inductive bias, which we …

In this paper, we present a novel differential morph detection framework, utilizing landmark
and appearance disentanglement. In our framework, the face image is represented in the …

We study a normalizing flow in the latent space of a top-down generator model, in which the
normalizing flow model plays the role of the informative prior model of the generator. We …

This paper studies the dynamic generator model for spatialtemporal processes such as
dynamic textures and action sequences in video data. In this model, each time frame of the …