630 research outputs found
New observational constraints on cosmology from radio quasars
Using a new recently compiled milliarcsecond compact radio data set of 120
intermediate-luminosity quasars in the redshift range , whose
statistical linear sizes show negligible dependence on redshifts and intrinsic
luminosity and thus represent standard rulers in cosmology, we constrain three
viable and most popular gravity models, where is the torsion scalar
in teleparallel gravity. Our analysis reveals that constraining power of the
quasars data (N=120) is comparable to the Union2.1 SN Ia data (N=580) for all
three models. Together with other standard ruler probes such as Cosmic
Microwave Background and Baryon Acoustic Oscillation distance measurements, the
present value of the matter density parameter obtained by quasars is
much lager than that derived from other observations. For two of the models
considered (CDM and CDM) a small but noticeable deviation from
CDM cosmology is present, while in the framework of CDM the
effective equation of state may cross the phantom divide line at lower
redshifts. These results indicate that intermediate-luminosity quasars could
provide an effective observational probe comparable to SN Ia at much higher
redsifts, and gravity is a reasonable candidate for the modified gravity
theory
Universal Adaptive Data Augmentation
Existing automatic data augmentation (DA) methods either ignore updating DA's
parameters according to the target model's state during training or adopt
update strategies that are not effective enough. In this work, we design a
novel data augmentation strategy called "Universal Adaptive Data Augmentation"
(UADA). Different from existing methods, UADA would adaptively update DA's
parameters according to the target model's gradient information during
training: given a pre-defined set of DA operations, we randomly decide types
and magnitudes of DA operations for every data batch during training, and
adaptively update DA's parameters along the gradient direction of the loss
concerning DA's parameters. In this way, UADA can increase the training loss of
the target networks, and the target networks would learn features from harder
samples to improve the generalization. Moreover, UADA is very general and can
be utilized in numerous tasks, e.g., image classification, semantic
segmentation and object detection. Extensive experiments with various models
are conducted on CIFAR-10, CIFAR-100, ImageNet, tiny-ImageNet, Cityscapes, and
VOC07+12 to prove the significant performance improvements brought by our
proposed adaptive augmentation.Comment: under submissio
Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware Generative Adversarial Network
3D-aware Generative Adversarial Networks (3D-GANs) currently exhibit
artifacts in their 3D geometrical modeling, such as mesh imperfections and
holes. These shortcomings are primarily attributed to the limited availability
of annotated 3D data, leading to a constrained "valid latent area" for
satisfactory modeling. To address this, we present a Self-Supervised Learning
(SSL) technique tailored as an auxiliary loss for any 3D-GAN, designed to
improve its 3D geometrical modeling capabilities. Our approach pioneers an
inversion technique for 3D-GANs, integrating an encoder that performs adaptive
spatially-varying range operations. Utilizing this inversion, we introduce the
Cyclic Generative Constraint (CGC), aiming to densify the valid latent space.
The CGC operates via augmented local latent vectors that maintain the same
geometric form, and it imposes constraints on the cycle path outputs,
specifically the generator-encoder-generator sequence. This SSL methodology
seamlessly integrates with the inherent GAN loss, ensuring the integrity of
pre-existing 3D-GAN architectures without necessitating alterations. We
validate our approach with comprehensive experiments across various datasets
and architectures, underscoring its efficacy. Our project website:
https://3dgan-ssl.github.ioComment: 13 pages, 12 figures, 6 table
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments
Deep Neural Networks (DNNs) are vulnerable to the black-box adversarial
attack that is highly transferable. This threat comes from the distribution gap
between adversarial and clean samples in feature space of the target DNNs. In
this paper, we use Deep Generative Networks (DGNs) with a novel training
mechanism to eliminate the distribution gap. The trained DGNs align the
distribution of adversarial samples with clean ones for the target DNNs by
translating pixel values. Different from previous work, we propose a more
effective pixel level training constraint to make this achievable, thus
enhancing robustness on adversarial samples. Further, a class-aware
feature-level constraint is formulated for integrated distribution alignment.
Our approach is general and applicable to multiple tasks, including image
classification, semantic segmentation, and object detection. We conduct
extensive experiments on different datasets. Our strategy demonstrates its
unique effectiveness and generality against black-box attacks
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