Search Results for author: Nanxiang Li

Found 5 papers, 1 papers with code

Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble

no code implementations1 Jan 2021 Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren

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

no code implementations27 Sep 2020 Liang Gou, Lincan Zou, Nanxiang Li, Michael Hofmann, Arvind Kumar Shekar, Axel Wendt, Liu Ren

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.

Autonomous Driving Decision Making +1

Improve Unsupervised Domain Adaptation with Mixup Training

1 code implementation3 Jan 2020 Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.

Domain Generalization Human Activity Recognition +2

Disentangled Representation Learning with Sequential Residual Variational Autoencoder

no code implementations ICLR 2020 Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren

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.


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