764 research outputs found
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Inspired by recent advances in deep learning, we propose a framework for
reconstructing MR images from undersampled data using a deep cascade of
convolutional neural networks to accelerate the data acquisition process. We
show that for Cartesian undersampling of 2D cardiac MR images, the proposed
method outperforms the state-of-the-art compressed sensing approaches, such as
dictionary learning-based MRI (DLMRI) reconstruction, in terms of
reconstruction error, perceptual quality and reconstruction speed for both
3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
method proposed is approximately twice as small, allowing to preserve
anatomical structures more faithfully. Using our method, each image can be
reconstructed in 23 ms, which is fast enough to enable real-time applications
SkipNet: Learning Dynamic Routing in Convolutional Networks
While deeper convolutional networks are needed to achieve maximum accuracy in
visual perception tasks, for many inputs shallower networks are sufficient. We
exploit this observation by learning to skip convolutional layers on a
per-input basis. We introduce SkipNet, a modified residual network, that uses a
gating network to selectively skip convolutional blocks based on the
activations of the previous layer. We formulate the dynamic skipping problem in
the context of sequential decision making and propose a hybrid learning
algorithm that combines supervised learning and reinforcement learning to
address the challenges of non-differentiable skipping decisions. We show
SkipNet reduces computation by 30-90% while preserving the accuracy of the
original model on four benchmark datasets and outperforms the state-of-the-art
dynamic networks and static compression methods. We also qualitatively evaluate
the gating policy to reveal a relationship between image scale and saliency and
the number of layers skipped.Comment: ECCV 2018 Camera ready version. Code is available at
https://github.com/ucbdrive/skipne
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