12 citations · 22 across the 12 of their papers we have counts for
4 papers · 1 filter
Neural Field Classifiers via Target Encoding and Classification Loss
Xindi Yang, Zeke Xie, Xiong Zhou +6
Neural field methods have seen great progress in various long-standing tasks in computer vision and computer graphics, including novel view synthesis and geometry reconstruction. A…
Learning Towards the Largest Margins
Xiong Zhou, Xianming Liu, Deming Zhai +3
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. Th…
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Chenyang Wang, Junjun Jiang, Xiong Zhou +1
Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet th…
Exploiting Invariance in Training Deep Neural Networks
Chengxi Ye, Xiong Zhou, Tristan McKinney +3
Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks. Th…