Search Results for author:
Found 5 papers, 1 papers with code
Driving in Real Life with Inverse Reinforcement Learning
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL).
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble
We show both theoretically and experimentally, the VAE ensemble objective encourages the linear transformations connecting the VAEs to be trivial transformations, aligning the latent representations of different models to be "alike".
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.
Improve Unsupervised Domain Adaptation with Mixup Training
Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.
Ranked #35 on
Domain Generalization
on PACS
Disentangled Representation Learning with Sequential Residual Variational Autoencoder
Recent advancements in unsupervised disentangled representation learning focus on extending the variational autoencoder (VAE) with an augmented objective function to balance the trade-off between disentanglement and reconstruction.
