5,422 research outputs found
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Neural architecture search (NAS) proves to be among the effective approaches
for many tasks by generating an application-adaptive neural architecture, which
is still challenged by high computational cost and memory consumption. At the
same time, 1-bit convolutional neural networks (CNNs) with binary weights and
activations show their potential for resource-limited embedded devices. One
natural approach is to use 1-bit CNNs to reduce the computation and memory cost
of NAS by taking advantage of the strengths of each in a unified framework,
while searching the 1-bit CNNs is more challenging due to the more complicated
processes involved. In this paper, we introduce Discrepant Child-Parent Neural
Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs, based on a new
framework of searching the 1-bit model (Child) under the supervision of a
real-valued model (Parent). Particularly, we first utilize a Parent model to
calculate a tangent direction, based on which the tangent propagation method is
introduced to search the optimized 1-bit Child. We further observe a coupling
relationship between the weights and architecture parameters existing in such
differentiable frameworks. To address the issue, we propose a decoupled
optimization method to search an optimized architecture. Extensive experiments
demonstrate that our DCP-NAS achieves much better results than prior arts on
both CIFAR-10 and ImageNet datasets. In particular, the backbones achieved by
our DCP-NAS achieve strong generalization performance on person
re-identification and object detection.Comment: Accepted by International Journal of Computer Visio
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
- …