39,529 research outputs found
Low-Shot Learning from Imaginary Data
Humans can quickly learn new visual concepts, perhaps because they can easily
visualize or imagine what novel objects look like from different views.
Incorporating this ability to hallucinate novel instances of new concepts might
help machine vision systems perform better low-shot learning, i.e., learning
concepts from few examples. We present a novel approach to low-shot learning
that uses this idea. Our approach builds on recent progress in meta-learning
("learning to learn") by combining a meta-learner with a "hallucinator" that
produces additional training examples, and optimizing both models jointly. Our
hallucinator can be incorporated into a variety of meta-learners and provides
significant gains: up to a 6 point boost in classification accuracy when only a
single training example is available, yielding state-of-the-art performance on
the challenging ImageNet low-shot classification benchmark.Comment: CVPR 2018 camera-ready versio
Variational ansatz-based quantum simulation of imaginary time evolution
Imaginary time evolution is a powerful tool for studying quantum systems.
While it is possible to simulate with a classical computer, the time and memory
requirements generally scale exponentially with the system size. Conversely,
quantum computers can efficiently simulate quantum systems, but not non-unitary
imaginary time evolution. We propose a variational algorithm for simulating
imaginary time evolution on a hybrid quantum computer. We use this algorithm to
find the ground-state energy of many-particle systems; specifically molecular
hydrogen and lithium hydride, finding the ground state with high probability.
Our method can also be applied to general optimisation problems and quantum
machine learning. As our algorithm is hybrid, suitable for error mitigation and
can exploit shallow quantum circuits, it can be implemented with current
quantum computers.Comment: 14 page
k-Space Deep Learning for Reference-free EPI Ghost Correction
Nyquist ghost artifacts in EPI are originated from phase mismatch between the
even and odd echoes. However, conventional correction methods using reference
scans often produce erroneous results especially in high-field MRI due to the
non-linear and time-varying local magnetic field changes. Recently, it was
shown that the problem of ghost correction can be reformulated as k-space
interpolation problem that can be solved using structured low-rank Hankel
matrix approaches. Another recent work showed that data driven Hankel matrix
decomposition can be reformulated to exhibit similar structures as deep
convolutional neural network. By synergistically combining these findings, we
propose a k-space deep learning approach that immediately corrects the phase
mismatch without a reference scan in both accelerated and non-accelerated EPI
acquisitions. To take advantage of the even and odd-phase directional
redundancy, the k-space data is divided into two channels configured with even
and odd phase encodings. The redundancies between coils are also exploited by
stacking the multi-coil k-space data into additional input channels. Then, our
k-space ghost correction network is trained to learn the interpolation kernel
to estimate the missing virtual k-space data. For the accelerated EPI data, the
same neural network is trained to directly estimate the interpolation kernels
for missing k-space data from both ghost and subsampling. Reconstruction
results using 3T and 7T in-vivo data showed that the proposed method
outperformed the image quality compared to the existing methods, and the
computing time is much faster.The proposed k-space deep learning for EPI ghost
correction is highly robust and fast, and can be combined with acceleration, so
that it can be used as a promising correction tool for high-field MRI without
changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin
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