New observational constraints on f(T)f(T) cosmology from radio quasars

Using a new recently compiled milliarcsecond compact radio data set of 120 intermediate-luminosity quasars in the redshift range $0.46< z <2.76$, 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 $f(T)$ gravity models, where $T$ 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 $f(T)$ 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 $\Omega_m$ obtained by quasars is much lager than that derived from other observations. For two of the models considered ($f_1$CDM and $f_2$CDM) a small but noticeable deviation from $\Lambda$CDM cosmology is present, while in the framework of $f_3$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 $f(T)$ 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