21 research outputs found
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
A Deep Choice Model for Hiring Outcome Prediction in Online Labor Markets
A key challenge faced by online labor market researchers and practitioners is to understand how employers make hiring decisions from many job bidders with distinct attributes. This study investigates employer hiring behavior in one of the largest online labor markets by building a datadriven hiring decision prediction model. With the limitation of traditional discrete choice model (conditional logit model), we develop a novel deep choice model to simulate the hiring behavior from 722,339 job posts. The deep choice model extends the classical conditional logit model by learning a non-linear utility function identically for each bidder within of the job posts via a pointwise convolutional neural network. This non-linear mapping can be straightforwardly optimized using stochastic gradient approach. We test the model on 12 categories of job posts in the dataset. Results show that our deep choice model outperforms the linear-utility conditional logit model in predicting hiring preferences. By analyzing the model using dimensionality reduction and sensitivity analysis, we highlight the nonlinear combination of bidders’ features in impacting employers’ hiring decisions
Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing
Optical fiber sensing is a technology wherein audio, vibrations, and
temperature are detected using an optical fiber; especially the
audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In
DAS, observed data, which is comprised of multichannel data, has suffered from
severe noise levels because of the optical noise or the installation methods.
In conventional methods for denoising DAS data, signal-processing- or
deep-neural-network (DNN)-based models have been studied. The
signal-processing-based methods have the interpretability, i.e., non-black box.
The DNN-based methods are good at flexibility designing network architectures
and objective functions, that is, priors. However, there is no balance between
the interpretability and the flexibility of priors in the DAS studies. The
DNN-based methods also require a large amount of training data in general. To
address the problems, we propose a DNN-structure signal-processing-based
denoising method in this paper. As the priors of DAS, we employ spatial
knowledge; low rank and channel-dependent sensitivity using the DNN-based
structure. The result of fiber-acoustic sensing shows that the proposed method
outperforms the conventional methods and the robustness to the number of the
spatial ranks. Moreover, the optimized parameters of the proposed method
indicate the relationship with the channel sensitivity; the interpretability.Comment: Accepted for ICASSP202
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Deep learning with 3D data has progressed significantly since the
introduction of convolutional neural networks that can handle point order
ambiguity in point cloud data. While being able to achieve good accuracies in
various scene understanding tasks, previous methods often have low training
speed and complex network architecture. In this paper, we address these
problems by proposing an efficient end-to-end permutation invariant convolution
for point cloud deep learning. Our simple yet effective convolution operator
named ShellConv uses statistics from concentric spherical shells to define
representative features and resolve the point order ambiguity, allowing
traditional convolution to perform on such features. Based on ShellConv we
further build an efficient neural network named ShellNet to directly consume
the point clouds with larger receptive fields while maintaining less layers. We
demonstrate the efficacy of ShellNet by producing state-of-the-art results on
object classification, object part segmentation, and semantic scene
segmentation while keeping the network very fast to train.Comment: International Conference on Computer Vision (ICCV) 2019 Ora