30 research outputs found
Further advantages of data augmentation on convolutional neural networks
Data augmentation is a popular technique largely used to enhance the training
of convolutional neural networks. Although many of its benefits are well known
by deep learning researchers and practitioners, its implicit regularization
effects, as compared to popular explicit regularization techniques, such as
weight decay and dropout, remain largely unstudied. As a matter of fact,
convolutional neural networks for image object classification are typically
trained with both data augmentation and explicit regularization, assuming the
benefits of all techniques are complementary. In this paper, we systematically
analyze these techniques through ablation studies of different network
architectures trained with different amounts of training data. Our results
unveil a largely ignored advantage of data augmentation: networks trained with
just data augmentation more easily adapt to different architectures and amount
of training data, as opposed to weight decay and dropout, which require
specific fine-tuning of their hyperparameters.Comment: Preprint of the manuscript accepted for presentation at the
International Conference on Artificial Neural Networks (ICANN) 2018. Best
Paper Awar
Hardening against adversarial examples with the smooth gradient method
Commonly used methods in deep learning do not utilise transformations of the residual gradient available at the inputs to update the representation in the dataset. It has been shown that this residual gradient, which can be interpreted as the first-order gradient of the input sensitivity at a particular point, may be used to improve generalisation in feed-forward neural networks, including fully connected and convolutional layers. We explore how these input gradients are related to input perturbations used to generate adversarial examples and how the networks that are trained with this technique are more robust to attacks generated with the fast gradient sign method
A multi-biometric iris recognition system based on a deep learning approach
YesMultimodal biometric systems have been widely
applied in many real-world applications due to its ability to
deal with a number of significant limitations of unimodal
biometric systems, including sensitivity to noise, population
coverage, intra-class variability, non-universality, and
vulnerability to spoofing. In this paper, an efficient and
real-time multimodal biometric system is proposed based
on building deep learning representations for images of
both the right and left irises of a person, and fusing the
results obtained using a ranking-level fusion method. The
trained deep learning system proposed is called IrisConvNet
whose architecture is based on a combination of Convolutional
Neural Network (CNN) and Softmax classifier to
extract discriminative features from the input image without
any domain knowledge where the input image represents
the localized iris region and then classify it into one of N
classes. In this work, a discriminative CNN training scheme
based on a combination of back-propagation algorithm and
mini-batch AdaGrad optimization method is proposed for
weights updating and learning rate adaptation, respectively.
In addition, other training strategies (e.g., dropout method,
data augmentation) are also proposed in order to evaluate
different CNN architectures. The performance of the proposed
system is tested on three public datasets collected
under different conditions: SDUMLA-HMT, CASIA-Iris-
V3 Interval and IITD iris databases. The results obtained
from the proposed system outperform other state-of-the-art
of approaches (e.g., Wavelet transform, Scattering transform,
Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases
and a recognition time less than one second per person
Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation
PubMed ID: 2313976