89 citations · 103 across the 11 of their papers we have counts for
4 papers · 1 filter
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Boqian Wu, Qiao Xiao, Shunxin Wang +5
It is generally perceived that Dynamic Sparse Training opens the door to a new era of scalability and efficiency for artificial neural networks at, perhaps, some costs in accuracy…
Are Sparse Neural Networks Better Hard Sample Learners?
Qiao Xiao, Boqian Wu, Lu Yin +4
While deep learning has demonstrated impressive progress, it remains a daunting challenge to learn from hard samples as these samples are usually noisy and intricate. These hard sa…
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
Boqian Wu, Qiao Xiao, Shiwei Liu +5
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computa…
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to…