270,833 research outputs found
Low-Cost Transfer Learning of Face Tasks
Do we know what the different filters of a face network represent? Can we use
this filter information to train other tasks without transfer learning? For
instance, can age, head pose, emotion and other face related tasks be learned
from face recognition network without transfer learning? Understanding the role
of these filters allows us to transfer knowledge across tasks and take
advantage of large data sets in related tasks. Given a pretrained network, we
can infer which tasks the network generalizes for and the best way to transfer
the information to a new task
Low-Cost Transfer Learning of Face Tasks
Do we know what the different filters of a face network
represent? Can we use this filter information to train other
tasks without transfer learning? For instance, can age, head
pose, emotion and other face related tasks be learned from
face recognition network without transfer learning? Understanding the role of these filters allows us to transfer
knowledge across tasks and take advantage of large data
sets in related tasks. Given a pretrained network, we can
infer which tasks the network generalizes for and the best
way to transfer the information to a new task.
We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was
not trained for. Our analysis proves these attributes can be
extracted with an accuracy comparable to what is obtained\ud
with transfer learning, but 10 times faster. We show that the
information about other tasks is present in relatively small
number of filters. We use these insights to do task specific
pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and
computational reduction of 60
Deep Bilateral Learning for Real-Time Image Enhancement
Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201
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