Discriminative Indexing for Probabilistic Image Patch Priors

Abstract. Newly emerged probabilistic image patch priors, such as Expected Patch Log-Likelihood (EPLL), have shown excellent performance on image restoration tasks, especially deconvolution, due to its rich expressiveness. However, its applicability is limited by the heavy computation involved in the associated optimization process. Inspired by the recent advances on using regression trees to index priors defined on a Conditional Random Field, we propose a novel discriminative indexing approach on patch-based priors to expedite the optimization process. Specifically, we propose an efficient tree indexing structure for EPLL, and overcome its training tractability challenges in high-dimensional spaces by utilizing special structures of the prior. Experimental results show that our approach accelerates state-of-the-art EPLL-based deconvolution methods by up to 40 times, with very little quality compromise.

Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

Digestive neural networks:A novel defense strategy against inference attacks in federated learning

Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog and edge computing. Its learning process employs frequent communications as the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them and redistributes new weights to the devices. In FL, private data does not leave the individual local devices, and thus, rendered as a robust solution in terms of privacy preservation. However, the recently introduced membership inference attacks pose a critical threat to the impeccability of FL mechanisms. By eavesdropping only on the updates transferring to the center server, these attacks can recover the private data of a local device. A prevalent solution against such attacks is the differential privacy scheme that augments a sufficient amount of noise to each update to hinder the recovering process. However, it suffers from a significant sacrifice in the classification accuracy of the FL. To effectively alleviate the problem, this paper proposes a Digestive Neural Network (DNN), an independent neural network attached to the FL. The private data owned by each device will pass through the DNN and then train the FL. The DNN modifies the input data, which results in distorting updates, in a way to maximize the classification accuracy of FL while the accuracy of inference attacks is minimized. Our simulation result shows that the proposed DNN shows significant performance on both gradient sharing- and weight sharing-based FL mechanisms. For the gradient sharing, the DNN achieved higher classification accuracy by 16.17% while 9% lower attack accuracy than the existing differential privacy schemes. For the weight sharing FL scheme, the DNN achieved at most 46.68% lower attack success rate with 3% higher classification accuracy

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Human Pose Estimation in Extremely Low-Light Conditions

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low light images, and extensive analyses validate that both of our model and dataset contribute to the success.Comment: Accepted to CVPR 202

Microsatellite-Based Genetic Diversity Among Three Duck Populations in Sumatera Island

This study aimed to determine the genetic diversity among three duck populations (Bayang, Pegagan, and Pitalah) reared in Sumatera island, Indonesia, using microsatellite markers. Genetic diversity among populations (n = 90) was determined using 22 microsatellite markers, based on several indices: number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), and Wright’s F-statistics ( ). The total number of alleles detected across loci was 121. The Na per locus ranged from 2 (APH24, CAUD128, and CAUD009) to 18 (CAUD048 and CAUD040). The mean Ho (0.429) dan He (0.509) indicated that the level of genetic diversity among populations was moderate, while the mean PIC (0.46) suggested that the tested loci were informative for assessing genetic diversity. The mean F-statistics ( ) were 0.148, 0.198, and 0.060, respectively. The  value indicated that the level of genetic differentiation among populations was moderate. The results confirms a moderate genetic diversity among populations, which could be beneficial for designing conservation and utilization of the local ducks in Sumatera island