574 citations · 667 across the 12 of their papers we have counts for
12 papers
Disentangled Unsupervised Image Translation via Restricted Information Flow
Ben Usman, Dina Bashkirova, Kate Saenko
Unsupervised image-to-image translation methods aim to map images from one domain into plausible examples from another domain while preserving structures shared across two domains.…
MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
Ben Usman, Andrea Tagliasacchi, Kate Saenko +1
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perf…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…
Evaluation of Correctness in Unsupervised Many-to-Many Image Translation
Dina Bashkirova, Ben Usman, Kate Saenko
Given an input image from a source domain and a guidance image from a target domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods seek to generate a plausi…
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
Ben Usman, Avneesh Sud, Nick Dufour +1
Distribution alignment has many applications in deep learning, including domain adaptation and unsupervised image-to-image translation. Most prior work on unsupervised distribution…
Adversarial Self-Defense for Cycle-Consistent GANs
Dina Bashkirova, Ben Usman, Kate Saenko
The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains. State-of-art method…