research
∙
07/14/2023
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation lear...
share
research
∙
02/09/2023
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained sett...
research
∙
08/19/2022
Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion
We consider the challenging problem of outdoor lighting estimation for t...
research
∙
07/04/2022
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more ...
research
∙
06/19/2022
Scalable Neural Data Server: A Data Recommender for Transfer Learning
Absence of large-scale labeled data in the practitioner's target domain ...
research
∙
02/10/2022
Domain Adversarial Training: A Game Perspective
The dominant line of work in domain adaptation has focused on learning i...
research
∙
01/20/2022
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Standard Federated Learning (FL) techniques are limited to clients with ...
research
∙
11/15/2021
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
Autonomous driving relies on a huge volume of real-world data to be labe...
research
∙
06/21/2021
f-Domain-Adversarial Learning: Theory and Algorithms
Unsupervised domain adaptation is used in many machine learning applicat...
research
∙
02/16/2021
Complex Momentum for Learning in Games
We generalize gradient descent with momentum for learning in differentia...
research
∙
01/09/2020
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
Transfer learning has proven to be a successful technique to train deep ...
research
∙
07/12/2019
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Current state-of-the-art methods for image segmentation form a dense ima...
research
∙
04/25/2019
Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large l...
research
∙
04/16/2019
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations
We tackle the problem of semantic boundary prediction, which aims to ide...
research
∙
10/23/2018
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
We present structured domain randomization (SDR), a variant of domain ra...
research
∙
04/18/2018
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
We present a system for training deep neural networks for object detecti...
research
∙
03/26/2018
