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Found 4 papers, 1 papers with code
Enhanced Sparsification via Stimulative Training
To alleviate this issue, we first study and reveal the relative sparsity effect in emerging stimulative training and then propose a structured pruning framework, named STP, based on an enhanced sparsification paradigm which maintains the magnitude of dropped weights and enhances the expressivity of kept weights by self-distillation.
Efficient Architecture Search via Bi-level Data Pruning
Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention.
SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution
Therefore, we propose a sparse baseline of VOS named SpVOS in this work, which develops a novel triple sparse convolution to reduce the computation costs of the overall VOS framework.
Boosting Residual Networks with Group Knowledge
Specifically, we implicitly divide all subnets into hierarchical groups by subnet-in-subnet sampling, aggregate the knowledge of different subnets in each group during training, and exploit upper-level group knowledge to supervise lower-level subnet groups.
