164 citations · 749 across the 21 of their papers we have counts for
6 papers · 1 filter
Identifying Model Weakness with Adversarial Examiner
Michelle Shu, Chenxi Liu, Weichao Qiu +1
Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g.…
Rethinking Normalization and Elimination Singularity in Neural Networks
Siyuan Qiao, Huiyu Wang, Chenxi Liu +2
In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the trainin…
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
Zhuotun Zhu, Chenxi Liu, Dong Yang +2
Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentati…
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Siyuan Qiao, Huiyu Wang, Chenxi Liu +2
Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typical…
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff +4
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this pap…
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Runtao Liu, Chenxi Liu, Yutong Bai +1
Referring object detection and referring image segmentation are important tasks that require joint understanding of visual information and natural language. Yet there has been evid…