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Awesome GitHub RepositoriesSelf-Modulating Refinement Blocks
Blocks that use a combination of self-modulation and partial convolutions to iteratively refine features.
Distinct from Refinement Modules: Combines self-modulation with specific architectural blocks like partial convolutions and residual connections.
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Awesome Self-Modulating Refinement Blocks GitHub Repositories
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- ai-dawang/plugnplay-modules
ai-dawang/PlugNPlay-Modules
4,968Vezi pe GitHubPlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural
Ships sequences of self-modulation and partial convolution blocks using residual connections to refine input features.
Python
