Search Results for author:
Found 13 papers, 8 papers with code
Disentangled Unsupervised Image Translation via Restricted Information Flow
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
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date.
Ranked #1 on
3D Human Pose Estimation
on SkiPose
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
Evaluation of Correctness in Unsupervised Many-to-Many Image Translation
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 plausible example from the target domain that preserves domain-invariant information of the input source image and inherits the domain-specific information from the guidance image.
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
We show that, under certain assumptions, this combination yields a deep neural likelihood-based minimization objective that attains a known lower bound upon convergence.
PuppetGAN: Cross-Domain Image Manipulation by Demonstration
In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation.
Adversarial Self-Defense for Cycle-Consistent GANs
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.
Cross-Domain Image Manipulation by Demonstration
In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation.
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
In this paper, we present a new large-scale benchmark called Syn2Real, which consists of a synthetic domain rendered from 3D object models and two real-image domains containing the same object categories.
Unsupervised Video-to-Video Translation
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.
VisDA: The Visual Domain Adaptation Challenge
We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions.
Tensor SimRank for Heterogeneous Information Networks
We propose a generalization of SimRank similarity measure for heterogeneous information networks.
