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Found 3 papers, 2 papers with code
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains
Neural network pruning techniques can substantially reduce the computational cost of applying convolutional neural networks (CNNs).
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
To overcome this issue, we propose Noise2Filter, a learned filter method that can be trained using only the measured data, and does not require any additional training data.
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Recovering a high-quality image from noisy indirect measurements is an important problem with many applications.
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