Search Results for author: Marc T. Law

Found 14 papers, 4 papers with code

Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting

no code implementations9 Feb 2023 Viraj Prabhu, David Acuna, Andrew Liao, Rafid Mahmood, Marc T. Law, Judy Hoffman, Sanja Fidler, James Lucas

Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain.

Autonomous Driving Domain Adaptation +3

Optimizing Data Collection for Machine Learning

no code implementations3 Oct 2022 Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law

Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect.

f-Domain-Adversarial Learning: Theory and Algorithms

1 code implementation21 Jun 2021 David Acuna, Guojun Zhang, Marc T. Law, Sanja Fidler

Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset.

Learning Theory Unsupervised Domain Adaptation

Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach

no code implementations5 Jun 2021 Rafid Mahmood, Sanja Fidler, Marc T. Law

Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label.

Active Learning

Self-Supervised Real-to-Sim Scene Generation

no code implementations ICCV 2021 Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law

Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.

Graph Generation Scene Generation +3

Ultrahyperbolic Representation Learning

1 code implementation NeurIPS 2020 Marc T. Law, Jos Stam

In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines.

Representation Learning

Video Face Clustering with Unknown Number of Clusters

1 code implementation ICCV 2019 Makarand Tapaswi, Marc T. Law, Sanja Fidler

Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing.

Clustering Face Clustering +1

Deep Spectral Clustering Learning

no code implementations ICML 2017 Marc T. Law, Raquel Urtasun, Richard S. Zemel

We derive a closed-form expression for the gradient that is efficient to compute: the complexity to compute the gradient is linear in the size of the training mini-batch and quadratic in the representation dimensionality.

Clustering Metric Learning

Closed-Form Training of Mahalanobis Distance for Supervised Clustering

no code implementations CVPR 2016 Marc T. Law, Yao-Liang Yu, Matthieu Cord, Eric P. Xing

Clustering is the task of grouping a set of objects so that objects in the same cluster are more similar to each other than to those in other clusters.

Clustering Metric Learning +2

Fantope Regularization in Metric Learning

no code implementations CVPR 2014 Marc T. Law, Nicolas Thome, Matthieu Cord

This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.

Face Verification General Classification +2

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