Search Results for author:
Found 11 papers, 4 papers with code
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet.
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.
Visualizing Classification Structure of Large-Scale Classifiers
We propose a measure to compute class similarity in large-scale classification based on prediction scores.
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 #22 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.
Controlling the Amount of Verbatim Copying in Abstractive Summarization
In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.
Ranked #10 on
Text Summarization
on GigaWord
Interpretable and Steerable Sequence Learning via Prototypes
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
Do Convolutional Neural Networks Learn Class Hierarchy?
We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.
Pose-Invariant Face Alignment with a Single CNN
Face alignment has witnessed substantial progress in the last decade.
Ranked #10 on
Facial Landmark Detection
on 300W
