111 citations · 734 across the 50 of their papers we have counts for
6 papers · 1 filter
Improved Fine-Tuning by Better Leveraging Pre-Training Data
Ziquan Liu, Yi Xu, Yuanhong Xu +5
As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studie…
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning
Zhirui Dai, Yuepeng Jiang, Yi Li +3
Social distancing, an essential public health measure to limit the spread of contagious diseases, has gained significant attention since the outbreak of the COVID-19 pandemic. In t…
Group-based Distinctive Image Captioning with Memory Attention
Jiuniu Wang, Wenjia Xu, Qingzhong Wang +1
Describing images using natural language is widely known as image captioning, which has made consistent progress due to the development of computer vision and natural language gene…
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
Xueying Zhan, Qing Li, Antoni B. Chan
Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., infor…
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
Ziquan Liu, Yufei Cui, Jia Wan +2
Weight decay is often used to ensure good generalization in the training practice of deep neural networks with batch normalization (BN-DNNs), where some convolution layers are inva…
Variational Nested Dropout
Yufei Cui, Yu Mao, Ziquan Liu +5
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training. It has been explored for:…