7,086 research outputs found
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%
FaceShop: Deep Sketch-based Face Image Editing
We present a novel system for sketch-based face image editing, enabling users
to edit images intuitively by sketching a few strokes on a region of interest.
Our interface features tools to express a desired image manipulation by
providing both geometry and color constraints as user-drawn strokes. As an
alternative to the direct user input, our proposed system naturally supports a
copy-paste mode, which allows users to edit a given image region by using parts
of another exemplar image without the need of hand-drawn sketching at all. The
proposed interface runs in real-time and facilitates an interactive and
iterative workflow to quickly express the intended edits. Our system is based
on a novel sketch domain and a convolutional neural network trained end-to-end
to automatically learn to render image regions corresponding to the input
strokes. To achieve high quality and semantically consistent results we train
our neural network on two simultaneous tasks, namely image completion and image
translation. To the best of our knowledge, we are the first to combine these
two tasks in a unified framework for interactive image editing. Our results
show that the proposed sketch domain, network architecture, and training
procedure generalize well to real user input and enable high quality synthesis
results without additional post-processing.Comment: 13 pages, 20 figure
Efficient Semidefinite Spectral Clustering via Lagrange Duality
We propose an efficient approach to semidefinite spectral clustering (SSC),
which addresses the Frobenius normalization with the positive semidefinite
(p.s.d.) constraint for spectral clustering. Compared with the original
Frobenius norm approximation based algorithm, the proposed algorithm can more
accurately find the closest doubly stochastic approximation to the affinity
matrix by considering the p.s.d. constraint. In this paper, SSC is formulated
as a semidefinite programming (SDP) problem. In order to solve the high
computational complexity of SDP, we present a dual algorithm based on the
Lagrange dual formalization. Two versions of the proposed algorithm are
proffered: one with less memory usage and the other with faster convergence
rate. The proposed algorithm has much lower time complexity than that of the
standard interior-point based SDP solvers. Experimental results on both UCI
data sets and real-world image data sets demonstrate that 1) compared with the
state-of-the-art spectral clustering methods, the proposed algorithm achieves
better clustering performance; and 2) our algorithm is much more efficient and
can solve larger-scale SSC problems than those standard interior-point SDP
solvers.Comment: 13 page
Learning to Personalize in Appearance-Based Gaze Tracking
Personal variations severely limit the performance of appearance-based gaze
tracking. Adapting to these variations using standard neural network model
adaptation methods is difficult. The problems range from overfitting, due to
small amounts of training data, to underfitting, due to restrictive model
architectures. We tackle these problems by introducing the SPatial Adaptive
GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional
latent parameter space, SPAZE provides just enough adaptability to capture the
range of personal variations without being prone to overfitting. Calibrating
SPAZE for a new person reduces to solving a small optimization problem. SPAZE
achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze,
improving on the state-of-the-art by 14 %. We contribute to gaze tracking
research by empirically showing that personal variations are well-modeled as a
3-dimensional latent parameter space for each eye. We show that this
low-dimensionality is expected by examining model-based approaches to gaze
tracking. We also show that accurate head pose-free gaze tracking is possible
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