369 research outputs found
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
The emergence of Deep Neural Networks (DNNs) has revolutionized various
domains, enabling the resolution of complex tasks spanning image recognition,
natural language processing, and scientific problem-solving. However, this
progress has also exposed a concerning vulnerability: adversarial examples.
These crafted inputs, imperceptible to humans, can manipulate machine learning
models into making erroneous predictions, raising concerns for safety-critical
applications. An intriguing property of this phenomenon is the transferability
of adversarial examples, where perturbations crafted for one model can deceive
another, often with a different architecture. This intriguing property enables
"black-box" attacks, circumventing the need for detailed knowledge of the
target model. This survey explores the landscape of the adversarial
transferability of adversarial examples. We categorize existing methodologies
to enhance adversarial transferability and discuss the fundamental principles
guiding each approach. While the predominant body of research primarily
concentrates on image classification, we also extend our discussion to
encompass other vision tasks and beyond. Challenges and future prospects are
discussed, highlighting the importance of fortifying DNNs against adversarial
vulnerabilities in an evolving landscape
Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection
Fingerprint recognition has been a hot research topic along the last few decades, with many applications and ever growing populations to identify. The need of flexible, fast identification systems is therefore patent in such situations. In this context, fingerprint classification is commonly used to improve the speed of the identification. This paper proposes a complete identification system with a hierarchical classification framework that fuses the information of multiple feature extractors. A feature selection is applied to improve the classification accuracy. Finally, the distributed identification is carried out with an incremental search, exploring the classes according to the probability order given by the classifier. A single parameter tunes the trade-off between identification time and accuracy. The proposal is evaluated over two NIST databases and a large synthetic database, yielding penetration rates close to the optimal values that can be reached with classification, leading to low identification times with small or no accuracy loss
Poincar\'e ResNet
This paper introduces an end-to-end residual network that operates entirely
on the Poincar\'e ball model of hyperbolic space. Hyperbolic learning has
recently shown great potential for visual understanding, but is currently only
performed in the penultimate layer(s) of deep networks. All visual
representations are still learned through standard Euclidean networks. In this
paper we investigate how to learn hyperbolic representations of visual data
directly from the pixel-level. We propose Poincar\'e ResNet, a hyperbolic
counterpart of the celebrated residual network, starting from Poincar\'e 2D
convolutions up to Poincar\'e residual connections. We identify three
roadblocks for training convolutional networks entirely in hyperbolic space and
propose a solution for each: (i) Current hyperbolic network initializations
collapse to the origin, limiting their applicability in deeper networks. We
provide an identity-based initialization that preserves norms over many layers.
(ii) Residual networks rely heavily on batch normalization, which comes with
expensive Fr\'echet mean calculations in hyperbolic space. We introduce
Poincar\'e midpoint batch normalization as a faster and equally effective
alternative. (iii) Due to the many intermediate operations in Poincar\'e
layers, we lastly find that the computation graphs of deep learning libraries
blow up, limiting our ability to train on deep hyperbolic networks. We provide
manual backward derivations of core hyperbolic operations to maintain
manageable computation graphs.Comment: International Conference on Computer Vision 202
Data-efficient deep representation learning
Current deep learning methods succeed in many data-intensive applications, but they are still not able to produce robust performance due to the lack of training samples. To investigate how to improve the performance of deep learning paradigms when training samples are limited, data-efficient deep representation learning (DDRL) is proposed in this study. DDRL as a sub area of representation learning mainly addresses the following problem: How can the performance of a deep learning method be maintained when the number of training samples is significantly reduced? This is vital for many applications where collecting data is highly costly, such as medical image analysis. Incorporating a certain kind of prior knowledge into the learning paradigm is key to achieving data efficiency.
Deep learning as a sub-area of machine learning can be divided into three parts (locations) in its learning process, namely Data, Optimisation and Model. Integrating prior knowledge into these three locations is expected to bring data efficiency into a learning paradigm, which can dramatically increase the model performance under the condition of limited training data.
In this thesis, we aim to develop novel deep learning methods for achieving data-efficient training, each of which integrates a certain kind of prior knowledge into three different locations respectively. We make the following contributions. First, we propose an iterative solution based on deep learning for medical image segmentation tasks, where dynamical systems are integrated into the segmentation labels in order to improve both performance and data efficiency. The proposed method not only shows a superior performance and better data efficiency compared to the state-of-the-art methods, but also has better interpretability and rotational invariance which are desired for medical imagining applications. Second, we propose a novel training framework which adaptively selects more informative samples for training during the optimization process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
We show that the proposed framework outperforms a random sampling method, which demonstrates effectiveness of the proposed framework. Thirdly, we propose a deep neural network model which produces the segmentation maps in a coarse-to-fine manner. The proposed architecture is a sequence of computational blocks containing a number of convolutional layers in which each block provides its successive block with a coarser segmentation map as a reference. Such mechanisms enable us to train the network with limited training samples and produce more interpretable results.Open Acces
A topological solution to object segmentation and tracking
The world is composed of objects, the ground, and the sky. Visual perception
of objects requires solving two fundamental challenges: segmenting visual input
into discrete units, and tracking identities of these units despite appearance
changes due to object deformation, changing perspective, and dynamic occlusion.
Current computer vision approaches to segmentation and tracking that approach
human performance all require learning, raising the question: can objects be
segmented and tracked without learning? Here, we show that the mathematical
structure of light rays reflected from environment surfaces yields a natural
representation of persistent surfaces, and this surface representation provides
a solution to both the segmentation and tracking problems. We describe how to
generate this surface representation from continuous visual input, and
demonstrate that our approach can segment and invariantly track objects in
cluttered synthetic video despite severe appearance changes, without requiring
learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary
material containing mathematical proof
Density estimation and modeling on symmetric spaces
In many applications, data and/or parameters are supported on non-Euclidean
manifolds. It is important to take into account the geometric structure of
manifolds in statistical analysis to avoid misleading results. Although there
has been a considerable focus on simple and specific manifolds, there is a lack
of general and easy-to-implement statistical methods for density estimation and
modeling on manifolds. In this article, we consider a very broad class of
manifolds: non-compact Riemannian symmetric spaces. For this class, we provide
a very general mathematical result for easily calculating volume changes of the
exponential and logarithm map between the tangent space and the manifold. This
allows one to define statistical models on the tangent space, push these models
forward onto the manifold, and easily calculate induced distributions by
Jacobians. To illustrate the statistical utility of this theoretical result, we
provide a general method to construct distributions on symmetric spaces. In
particular, we define the log-Gaussian distribution as an analogue of the
multivariate Gaussian distribution in Euclidean space. With these new kernels
on symmetric spaces, we also consider the problem of density estimation. Our
proposed approach can use any existing density estimation approach designed for
Euclidean spaces and push it forward to the manifold with an easy-to-calculate
adjustment. We provide theorems showing that the induced density estimators on
the manifold inherit the statistical optimality properties of the parent
Euclidean density estimator; this holds for both frequentist and Bayesian
nonparametric methods. We illustrate the theory and practical utility of the
proposed approach on the space of positive definite matrices
Statistical Computing on Non-Linear Spaces for Computational Anatomy
International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings
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