Seyed Iranmanesh — accepted AI-conference papers

2018

Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound

Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Iranmanesh, Nasser Nasrabadi

NeurIPS 2018poster

Unsupervised image-to-image translation is a class of computer vision problems which aims at modeling conditional distribution of images in the target domain, given a set of unpaired images in the source and target domains. An image in the source domain might have multiple representations in the tar…

Cited by 53 Source ↗PDF ↗Scholar ↗

2017

Few-Shot Adversarial Domain Adaptation

Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto

NeurIPS 2017poster

This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The…

Cited by 435 Source ↗PDF ↗Scholar ↗