Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification

In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently fine-tune the low-level layers of the network due to the gradient vanishing problem. In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights. Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks. The improved performance of the proposed strategy is validated via several experiments. Furthermore, without any add-ons such as pose estimation or segmentation, our strategy exhibits state-of-the-art performance using only vanilla deep convolutional neural network architecture.Comment: Accepted to AAAI 201

Over-Fit: Noisy-Label Detection based on the Overfitted Model Property

Due to the increasing need to handle the noisy label problem in a massive dataset, learning with noisy labels has received much attention in recent years. As a promising approach, there have been recent studies to select clean training data by finding small-loss instances before a deep neural network overfits the noisy-label data. However, it is challenging to prevent overfitting. In this paper, we propose a novel noisy-label detection algorithm by employing the property of overfitting on individual data points. To this end, we present two novel criteria that statistically measure how much each training sample abnormally affects the model and clean validation data. Using the criteria, our iterative algorithm removes noisy-label samples and retrains the model alternately until no further performance improvement is made. In experiments on multiple benchmark datasets, we demonstrate the validity of our algorithm and show that our algorithm outperforms the state-of-the-art methods when the exact noise rates are not given. Furthermore, we show that our method can not only be expanded to a real-world video dataset but also can be viewed as a regularization method to solve problems caused by overfitting.Comment: 10 pages, 7 figure

Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System

A surrogate model that accurately predicts distribution system voltages is crucial for reliable smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate modeling method that employs a limited dataset to learn the power-voltage relationship of an unbalanced three-phase distribution system. The proposed surrogate model is designed using a fixed-point load-flow equation, and the stochastic gradient descent method with an automatic differentiation technique is employed to update the parameters of the surrogate model using complex power and voltage samples. Numerical examples in IEEE 13-bus, 37-bus, and 123-bus systems demonstrate that the proposed surrogate model can outperform surrogate models based on the deep neural network and Gaussian process regarding prediction accuracy and sample efficiencyComment: Under review in IEEE PES Lette

Transduction of Cu, Zn-superoxide dismutase mediated by an HIV-1 Tat protein basic domain into human chondrocytes

This study was performed to investigate the transduction of a full-length superoxide dismutase (SOD) protein fused to transactivator of transcription (Tat) into human chondrocytes, and to determine the regulatory function of transduced Tat-SOD in the inflammatory cytokine induced catabolic pathway. The pTat-SOD expression vector was constructed to express the basic domain of HIV-1 Tat as a fusion protein with Cu, Zn-SOD. We also purified histidine-tagged SOD without an HIV-1 Tat and Tat-GFP as control proteins. Cartilage samples were obtained from patients with osteoarthritis (OA) and chondrocytes were cultured in both a monolayer and an explant. For the transduction of fusion proteins, cells/explants were treated with a variety of concentrations of fusion proteins. The transduced protein was detected by fluorescein labeling, western blotting and SOD activity assay. Effects of transduced Tat-SOD on the regulation of IL-1 induced nitric oxide (NO) production and inducible nitric oxide synthase (iNOS) mRNA expression was assessed by the Griess reaction and reverse transcriptase PCR, respectively. Tat-SOD was successfully delivered into both the monolayer and explant cultured chondrocytes, whereas the control SOD was not. The intracellular transduction of Tat-SOD into cultured chondrocytes was detected after 1 hours, and the amount of transduced protein did not change significantly after further incubation. SOD enzyme activity increased in a dose-dependent manner. NO production and iNOS mRNA expression, in response to IL-1 stimulation, was significantly down-regulated by pretreatment with Tat-SOD fusion proteins. This study shows that protein delivery employing the Tat-protein transduction domain is feasible as a therapeutic modality to regulate catabolic processes in cartilage. Construction of additional Tat-fusion proteins that can regulate cartilage metabolism favorably and application of this technology in in vivo models of arthritis are the subjects of future studies

Class-Attentive Diffusion Network for Semi-Supervised Classification

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.Comment: Accepted to AAAI 202

Oppositely rotating eigenmodes of spin-polarized current-driven vortex gyrotropic motions in elliptical nanodots

The authors found that there exist two different rotational eigenmodes of oppositely rotating sense in spin-polarized current-driven vortex gyrotropic motions in soft magnetic elliptical nanodots. Simple mathematical expressions were analytically calculated by adopting vortex-core (VC)-rotation-sense- dependent dynamic susceptibility tensors based on the linearized Thiele equation [Phys. Rev. Lett. 30, 230 (1973)]. The numerical calculations of those analytical expressions were confirmed by micromagnetic simulations, revealing that linear-regime steady-state VC motions driven by any polarized oscillating currents can be interpreted simply by the superposition of the clockwise and counterclockwise rotational eigenmodes. The shape of the orbital trajectories of the two eigenmodes is determined only by the lateral dimension of elliptical dots. Additionally, the orbital radii and phases of the two eigenmodes' VC motions were found to markedly vary with the frequency of applied currents, particularly across the vortex eigenfrequency and according to the vortex polarization, which results in overall VC motions driven by any polarized oscillating currents.open8