Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

no code implementations • 4 Jul 2019 • Alex Gabourie, Mohammad Rostami, Philip Pope, Soheil Kolouri, Kyungnam Kim

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 two source and target visual domains is minimized.

Unsupervised Domain Adaptation

Image to Image Translation for Domain Adaptation

no code implementations • CVPR 2018 • Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, Kyungnam Kim

This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network.

Image-to-Image Translation Translation +1

Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition

no code implementations • 15 Sep 2017 • Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton

Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience.

Multi-Task Learning

Joint Dictionaries for Zero-Shot Learning

no code implementations • 12 Sep 2017 • Soheil Kolouri, Mohammad Rostami, Yuri Owechko, Kyungnam Kim

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 of data (e. g. visual data).

Attribute Dictionary Learning +1

Zero Shot Learning via Multi-Scale Manifold Regularization

no code implementations • CVPR 2017 • Shay Deutsch, Soheil Kolouri, Kyungnam Kim, Yuri Owechko, Stefano Soatto

We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs.

Zero-Shot Learning