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Found 3 papers, 3 papers with code
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs).
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC).
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.
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