511 research outputs found
Automated Data Augmentations for Graph Classification
Data augmentations are effective in improving the invariance of learning
machines. We argue that the corechallenge of data augmentations lies in
designing data transformations that preserve labels. This is
relativelystraightforward for images, but much more challenging for graphs. In
this work, we propose GraphAug, a novelautomated data augmentation method
aiming at computing label-invariant augmentations for graph
classification.Instead of using uniform transformations as in existing studies,
GraphAug uses an automated augmentationmodel to avoid compromising critical
label-related information of the graph, thereby producing
label-invariantaugmentations at most times. To ensure label-invariance, we
develop a training method based on reinforcementlearning to maximize an
estimated label-invariance probability. Comprehensive experiments show that
GraphAugoutperforms previous graph augmentation methods on various graph
classification tasks
solution to an open problem about a transformation on the space of copulas
AbstractWe solve a recent open problem about a new transformation mapping the set of copulas into itself. The obtained mapping is characterized in algebraic terms and some limit results are proved
Sandpiles and Dominos
We consider the subgroup of the abelian sandpile group of the grid graph
consisting of configurations of sand that are symmetric with respect to central
vertical and horizontal axes. We show that the size of this group is (i) the
number of domino tilings of a corresponding weighted rectangular checkerboard;
(ii) a product of special values of Chebyshev polynomials; and (iii) a
double-product whose factors are sums of squares of values of trigonometric
functions. We provide a new derivation of the formula due to Kasteleyn and to
Temperley and Fisher for counting the number of domino tilings of a 2m x 2n
rectangular checkerboard and a new way of counting the number of domino tilings
of a 2m x 2n checkerboard on a M\"obius strip.Comment: 35 pages, 24 figure
ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain
Transformer design is the de facto standard for natural language processing
tasks. The success of the transformer design in natural language processing has
lately piqued the interest of researchers in the domain of computer vision.
When compared to Convolutional Neural Networks (CNNs), Vision Transformers
(ViTs) are becoming more popular and dominant solutions for many vision
problems. Transformer-based models outperform other types of networks, such as
convolutional and recurrent neural networks, in a range of visual benchmarks.
We evaluate various vision transformer models in this work by dividing them
into distinct jobs and examining their benefits and drawbacks. ViTs can
overcome several possible difficulties with convolutional neural networks
(CNNs). The goal of this survey is to show the first use of ViTs in CV. In the
first phase, we categorize various CV applications where ViTs are appropriate.
Image classification, object identification, image segmentation, video
transformer, image denoising, and NAS are all CV applications. Our next step
will be to analyze the state-of-the-art in each area and identify the models
that are currently available. In addition, we outline numerous open research
difficulties as well as prospective research possibilities.Comment: ICCD-2023. arXiv admin note: substantial text overlap with
arXiv:2208.04309 by other author
Vision-based techniques for automatic marine plankton classification
Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de Málaga / CBUA.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
The authors wish to thank Alonso Hernández-Guerra for his frm support in the
development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain
of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement
817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation
Programme and the Ministry of Science from the Spanish Government through the Project DESAFÍO
(PID2020-118118RB-I00)
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially by utilizing deep learning methods. This survey is
an effort to provide a detailed survey of recent progress in single-image
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for image super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Some of the reviewed state-of-the-art image SR methods
include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN),
multiscale residual network (MSRN), meta residual dense network (Meta-RDN),
recurrent back-projection network (RBPN), second-order attention network (SAN),
SR feedback network (SRFBN) and the wavelet-based residual attention network
(WRAN). Finally, this survey is concluded with future directions and trends in
SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table
- …