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Found 7 papers, 2 papers with code
Language Conditioned Traffic Generation
In this work, we turn to language as a source of supervision for dynamic traffic scene generation.
SceneGen: Learning to Generate Realistic Traffic Scenes
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
Improving the Fairness of Deep Generative Models without Retraining
Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data.
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
Class-imbalanced Domain Adaptation: An Empirical Odyssey
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains.
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment
In this paper, we explore the task of Generalized Domain Adaptation (GDA): How to transfer knowledge across different domains in the presence of both covariate and label shift?
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Thus, it is meaningful to let partially labeled domains learn from each other to classify all the unlabeled samples in each domain under an open-set setting.
