25 citations · 35 across the 4 of their papers we have counts for
5 papers
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
Alex Gabourie, Mohammad Rostami, Philip Pope +2
We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the…
Zero-Shot Image Classification Using Coupled Dictionary Embedding
Mohammad Rostami, Soheil Kolouri, Zak Murez +3
Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes. In this paper, we propose a…
Image to Image Translation for Domain Adaptation
Zak Murez, Soheil Kolouri, David Kriegman +2
We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without re…
Joint Dictionaries for Zero-Shot Learning
Soheil Kolouri, Mohammad Rostami, Yuri Owechko +1
A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes…
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition
Mohammad Rostami, Soheil Kolouri, Kyungnam Kim +1
Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience. Current lifelong learning algorithms rely upon a…