172 citations · 559 across the 44 of their papers we have counts for
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Explanation by Progressive Exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen +1
As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation…
Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement
Junxiang Chen, Kayhan Batmanghelich
Recent work by Locatello et al. (2018) has shown that an inductive bias is required to disentangle factors of interest in Variational Autoencoder (VAE). Motivated by a real-world p…
Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning
Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich +1
Deep learning-based approaches achieve state-of-the-art performance in the majority of image segmentation benchmarks. However, training of such models requires a sizable amount of…
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Yipeng Mou, Mingming Gong, Huan Fu +3
Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…
Twin Auxiliary Classifiers GAN
Mingming Gong, Yanwu Xu, Chunyuan Li +2
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly…
Weakly Supervised Disentanglement by Pairwise Similarities
Junxiang Chen, Kayhan Batmanghelich
Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, ther…
Generative-Discriminative Complementary Learning
Yanwu Xu, Mingming Gong, Junxiang Chen +3
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such appr…
Robust Angular Local Descriptor Learning
Yanwu Xu, Mingming Gong, Tongliang Liu +2
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-…