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

UGPNet: Universal Generative Prior for Image Restoration

Recent image restoration methods can be broadly categorized into two classes: (1) regression methods that recover the rough structure of the original image without synthesizing high-frequency details and (2) generative methods that synthesize perceptually-realistic high-frequency details even though the resulting image deviates from the original structure of the input. While both directions have been extensively studied in isolation, merging their benefits with a single framework has been rarely studied. In this paper, we propose UGPNet, a universal image restoration framework that can effectively achieve the benefits of both approaches by simply adopting a pair of an existing regression model and a generative model. UGPNet first restores the image structure of a degraded input using a regression model and synthesizes a perceptually-realistic image with a generative model on top of the regressed output. UGPNet then combines the regressed output and the synthesized output, resulting in a final result that faithfully reconstructs the structure of the original image in addition to perceptually-realistic textures. Our extensive experiments on deblurring, denoising, and super-resolution demonstrate that UGPNet can successfully exploit both regression and generative methods for high-fidelity image restoration.Comment: Accepted to WACV 202

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

Linearly Replaceable Filters for Deep Network Channel Pruning

Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining attention for general applications in various fields. This paper proposes a novel channel pruning method, Linearly Replaceable Filter (LRF), which suggests that a filter that can be approximated by the linear combination of other filters is replaceable. Moreover, an additional method called Weights Compensation is proposed to support the LRF method. This is a technique that effectively reduces the output difference caused by removing filters via direct weight modification. Through various experiments, we have confirmed that our method achieves state-of-the-art performance in several benchmarks. In particular, on ImageNet, LRF-60 reduces approximately 56% of FLOPs on ResNet-50 without top-5 accuracy drop. Further, through extensive analyses, we proved the effectiveness of our approaches

Synthesis of the C1–C10 Fragment of Madeirolide A

The synthesis of a fully elaborated C1–C10 fragment of madeirolide A has been achieved via a strategy based on a series of stereospecific processes. The concise synthetic route also features an iridium-catalyzed visible light induced radical cyclization for construction of the THP ring and a palladium-catalyzed glycosylation for formation of the α-cineruloside linkage

DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains

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Diagnostic Performance of a Molecular Assay in Synovial Fluid Targeting Dominant Prosthetic Joint Infection Pathogens

Prosthetic joint infection (PJI) is one of the most serious complications of joint replacement surgery among orthopedic surgeries and occurs in 1 to 2% of primary surgeries. Additionally, the cause of PJIs is mostly bacteria from the Staphylococcus species, accounting for more than 98%, while fungi cause PJIs in only 1 to 2% of cases and can be difficult to manage. The current gold-standard microbiological method of culturing synovial fluid is time-consuming and produces false-negative and -positive results. This study aimed to identify a novel, accurate, and convenient molecular diagnostic method. The DreamDX primer–hydrolysis probe set was designed for the pan-bacterial and pan-fungal detection of DNA from pathogens that cause PJIs. The sensitivity and specificity of DreamDX primer–hydrolysis probes were 88.89% (95% CI, 56.50–99.43%) and 97.62% (95% CI, 87.68–99.88%), respectively, compared with the microbiological method of culturing synovial fluid, and receiver operating characteristic (ROC) area under the curve (AUC) was 0.9974 (*** p < 0.0001). It could be concluded that the DreamDX primer–hydrolysis probes have outstanding potential as a molecular diagnostic method for identifying the causative agents of PJIs, and that host inflammatory markers are useful as adjuvants in the diagnosis of PJIs

Dr.3D: Adapting 3D GANs to Artistic Drawings

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BigColor: Colorization using a Generative Color Prior for Natural Images

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