10,601 research outputs found
Efficient Gender Classification Using a Deep LDA-Pruned Net
Many real-time tasks, such as human-computer interaction, require fast and
efficient facial gender classification. Although deep CNN nets have been very
effective for a multitude of classification tasks, their high space and time
demands make them impractical for personal computers and mobile devices without
a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural
network which boosts efficiency while maintaining high accuracy. Our net is
pruned from the VGG-16 model starting from the last convolutional (conv) layer
where we find neuron activations are highly uncorrelated given the gender.
Through Fisher's Linear Discriminant Analysis (LDA), we show that this high
decorrelation makes it safe to discard directly last conv layer neurons with
high within-class variance and low between-class variance. Combined with either
Support Vector Machines (SVM) or Bayesian classification, the reduced CNNs are
capable of achieving comparable (or even higher) accuracies on the LFW and
CelebA datasets than the original net with fully connected layers. On LFW, only
four Conv5_3 neurons are able to maintain a comparably high recognition
accuracy, which results in a reduction of total network size by a factor of 70X
with a 11 fold speedup. Comparisons with a state-of-the-art pruning method as
well as two smaller nets in terms of accuracy loss and convolutional layers
pruning rate are also provided.Comment: The only difference with the previous version v2 is the title on the
arxiv page. I am changing it back to the original title in v1 because
otherwise google scholar cannot track the citations to this arxiv paper
correctly. You could cite either the conference version or this arxiv
version. They are equivalen
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the
inverse problems. The inverse problem is cast in the form of learning an
end-to-end mapping from observed data to the ground-truth. Inspired by the
splitting strategy widely used in regularized iterative algorithm to tackle
inverse problems, the mapping is decomposed into two networks, with one
handling the inversion of the physical forward model associated with the data
term and one handling the denoising of the output from the former network,
i.e., the inverted version, associated with the prior/regularization term. The
two networks are trained jointly to learn the end-to-end mapping, getting rid
of a two-step training. The training is annealing as the intermediate variable
between these two networks bridges the gap between the input (the degraded
version of output) and output and progressively approaches to the ground-truth.
The proposed network, referred to as InverseNet, is flexible in the sense that
most of the existing end-to-end network structure can be leveraged in the first
network and most of the existing denoising network structure can be used in the
second one. Extensive experiments on both synthetic data and real datasets on
the tasks, motion deblurring, super-resolution, and colorization, demonstrate
the efficiency and accuracy of the proposed method compared with other image
processing algorithms
Predicting online user behaviour using deep learning algorithms
We propose a robust classifier to predict buying intentions based on user
behaviour within a large e-commerce website. In this work we compare
traditional machine learning techniques with the most advanced deep learning
approaches. We show that both Deep Belief Networks and Stacked Denoising
auto-Encoders achieved a substantial improvement by extracting features from
high dimensional data during the pre-train phase. They prove also to be more
convenient to deal with severe class imbalance.Comment: 21 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1412.6601, arXiv:1406.1231, arXiv:1508.03856 by other author
Comparison of 14 different families of classification algorithms on 115 binary datasets
We tested 14 very different classification algorithms (random forest,
gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer
neural nets, extreme learning machines, k-nearest neighbors and a bagging of
knn, naive Bayes, learning vector quantization, elastic net logistic
regression, sparse linear discriminant analysis, and a boosting of linear
classifiers) on 115 real life binary datasets. We followed the Demsar analysis
and found that the three best classifiers (random forest, gbm and RBF SVM) are
not significantly different from each other. We also discuss that a change of
less then 0.0112 in the error rate should be considered as an irrelevant
change, and used a Bayesian ANOVA analysis to conclude that with high
probability the differences between these three classifiers is not of practical
consequence. We also verified the execution time of "standard implementations"
of these algorithms and concluded that RBF SVM is the fastest (significantly
so) both in training time and in training plus testing time
Deep Regression Bayesian Network and Its Applications
Deep directed generative models have attracted much attention recently due to
their generative modeling nature and powerful data representation ability. In
this paper, we review different structures of deep directed generative models
and the learning and inference algorithms associated with the structures. We
focus on a specific structure that consists of layers of Bayesian Networks due
to the property of capturing inherent and rich dependencies among latent
variables. The major difficulty of learning and inference with deep directed
models with many latent variables is the intractable inference due to the
dependencies among the latent variables and the exponential number of latent
variable configurations. Current solutions use variational methods often
through an auxiliary network to approximate the posterior probability
inference. In contrast, inference can also be performed directly without using
any auxiliary network to maximally preserve the dependencies among the latent
variables. Specifically, by exploiting the sparse representation with the
latent space, max-max instead of max-sum operation can be used to overcome the
exponential number of latent configurations. Furthermore, the max-max operation
and augmented coordinate ascent are applied to both supervised and unsupervised
learning as well as to various inference. Quantitative evaluations on benchmark
datasets of different models are given for both data representation and feature
learning tasks.Comment: Accepted to IEEE Signal Processing Magazin
Evolutionary algorithms in genetic regulatory networks model
Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of
complex biological processes. Modeling GRNs is significantly important in order
to reveal fundamental cellular processes, examine gene functions and
understanding their complex relationships. Understanding the interactions
between genes gives rise to develop better method for drug discovery and
diagnosis of the disease since many diseases are characterized by abnormal
behaviour of the genes. In this paper we have reviewed various evolutionary
algorithms-based approach for modeling GRNs and discussed various opportunities
and challenges.Comment: 10 pages, 3 figures and 1 tabl
Multi-task Neural Networks for QSAR Predictions
Although artificial neural networks have occasionally been used for
Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in
the past, the literature has of late been dominated by other machine learning
techniques such as random forests. However, a variety of new neural net
techniques along with successful applications in other domains have renewed
interest in network approaches. In this work, inspired by the winning team's
use of neural networks in a recent QSAR competition, we used an artificial
neural network to learn a function that predicts activities of compounds for
multiple assays at the same time. We conducted experiments leveraging recent
methods for dealing with overfitting in neural networks as well as other tricks
from the neural networks literature. We compared our methods to alternative
methods reported to perform well on these tasks and found that our neural net
methods provided superior performance
Limits of Deepfake Detection: A Robust Estimation Viewpoint
Deepfake detection is formulated as a hypothesis testing problem to classify
an image as genuine or GAN-generated. A robust statistics view of GANs is
considered to bound the error probability for various GAN implementations in
terms of their performance. The bounds are further simplified using a Euclidean
approximation for the low error regime. Lastly, relationships between error
probability and epidemic thresholds for spreading processes in networks are
established
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Generative Adversarial
Networks (GAN) are generative neural networks which can be trained to
implicitly model the probability distribution of given data, and it is possible
to sample this distribution. We integrate a GAN into an EDA and evaluate the
performance of this system when solving combinatorial optimization problems
with a single objective. We use several standard benchmark problems and compare
the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield
competitive results - the GAN lacks the ability to quickly learn a good
approximation of the probability distribution. A key reason seems to be the
large amount of noise present in the first EDA generations
A Gentle Introduction to Deep Learning in Medical Image Processing
This paper tries to give a gentle introduction to deep learning in medical
image processing, proceeding from theoretical foundations to applications. We
first discuss general reasons for the popularity of deep learning, including
several major breakthroughs in computer science. Next, we start reviewing the
fundamental basics of the perceptron and neural networks, along with some
fundamental theory that is often omitted. Doing so allows us to understand the
reasons for the rise of deep learning in many application domains. Obviously
medical image processing is one of these areas which has been largely affected
by this rapid progress, in particular in image detection and recognition, image
segmentation, image registration, and computer-aided diagnosis. There are also
recent trends in physical simulation, modelling, and reconstruction that have
led to astonishing results. Yet, some of these approaches neglect prior
knowledge and hence bear the risk of producing implausible results. These
apparent weaknesses highlight current limitations of deep learning. However, we
also briefly discuss promising approaches that might be able to resolve these
problems in the future.Comment: Accepted by Journal of Medical Physics; Final Version after revie
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