24 citations · 35 across the 9 of their papers we have counts for
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cs.CV2021★ 24 cited
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…
cs.LG2021★ 1 cited
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…
cs.LG2021
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:…