10 research outputs found
Few-shot Image Classification based on Gradual Machine Learning
Few-shot image classification aims to accurately classify unlabeled images
using only a few labeled samples. The state-of-the-art solutions are built by
deep learning, which focuses on designing increasingly complex deep backbones.
Unfortunately, the task remains very challenging due to the difficulty of
transferring the knowledge learned in training classes to new ones. In this
paper, we propose a novel approach based on the non-i.i.d paradigm of gradual
machine learning (GML). It begins with only a few labeled observations, and
then gradually labels target images in the increasing order of hardness by
iterative factor inference in a factor graph. Specifically, our proposed
solution extracts indicative feature representations by deep backbones, and
then constructs both unary and binary factors based on the extracted features
to facilitate gradual learning. The unary factors are constructed based on
class center distance in an embedding space, while the binary factors are
constructed based on k-nearest neighborhood. We have empirically validated the
performance of the proposed approach on benchmark datasets by a comparative
study. Our extensive experiments demonstrate that the proposed approach can
improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is
more robust than the existing deep models in that its performance can
consistently improve as the size of query set increases while the performance
of deep models remains essentially flat or even becomes worse.Comment: 17 pages,6 figures,5 tables, 55 conference
CAT:Collaborative Adversarial Training
Adversarial training can improve the robustness of neural networks. Previous
methods focus on a single adversarial training strategy and do not consider the
model property trained by different strategies. By revisiting the previous
methods, we find different adversarial training methods have distinct
robustness for sample instances. For example, a sample instance can be
correctly classified by a model trained using standard adversarial training
(AT) but not by a model trained using TRADES, and vice versa. Based on this
observation, we propose a collaborative adversarial training framework to
improve the robustness of neural networks. Specifically, we use different
adversarial training methods to train robust models and let models interact
with their knowledge during the training process. Collaborative Adversarial
Training (CAT) can improve both robustness and accuracy. Extensive experiments
on various networks and datasets validate the effectiveness of our method. CAT
achieves state-of-the-art adversarial robustness without using any additional
data on CIFAR-10 under the Auto-Attack benchmark. Code is available at
https://github.com/liuxingbin/CAT.Comment: Tech repor
Latent Feature Relation Consistency for Adversarial Robustness
Deep neural networks have been applied in many computer vision tasks and
achieved state-of-the-art performance. However, misclassification will occur
when DNN predicts adversarial examples which add human-imperceptible
adversarial noise to natural examples. This limits the application of DNN in
security-critical fields. To alleviate this problem, we first conducted an
empirical analysis of the latent features of both adversarial and natural
examples and found the similarity matrix of natural examples is more compact
than those of adversarial examples. Motivated by this observation, we propose
\textbf{L}atent \textbf{F}eature \textbf{R}elation \textbf{C}onsistency
(\textbf{LFRC}), which constrains the relation of adversarial examples in
latent space to be consistent with the natural examples. Importantly, our LFRC
is orthogonal to the previous method and can be easily combined with them to
achieve further improvement. To demonstrate the effectiveness of LFRC, we
conduct extensive experiments using different neural networks on benchmark
datasets. For instance, LFRC can bring 0.78\% further improvement compared to
AT, and 1.09\% improvement compared to TRADES, against AutoAttack on CIFAR10.
Code is available at https://github.com/liuxingbin/LFRC.Comment: Tech repor
Characterization of internalin genes in Listeria monocytogenes from food and humans, and their association with the invasion of Caco-2 cells
Internalins are surface proteins that are utilized by Listeria monocytogenes to facilitate its invasion into human intestinal epithelial cells. The expression of a full-length InlA is one of essential virulence factors for L. monocytogenes to cross the intestinal barrier in order to invade epithelial cells.https://doi.org/10.1186/s13099-019-0307-
The Impact of Haze Pollution on Chinese Local Government Debt
Can haze pollution effectively influence local government debt? This study uses data from 285 Chinese cities at the prefecture level or above from 2006 to 2019, and uses annual average sunny days as an instrumental variable to systematically analyze the impact of haze pollution on local government debt from both theoretical and empirical perspectives. The main conclusions of this study are: (1) there is a positive correlation between haze pollution and local government debt; (2) sub-sample analysis suggests that the impact of haze pollution on local government debt shows heterogeneous effects in different cities and at different times; and (3) the analysis of the relevant mechanism suggests that haze pollution can promote local government debt through industrial structure upgrading and green technology innovation. The research conclusions provide policy references for Chinese local governments to effectively respond to haze pollution
The miR-641-STIM1 and SATB1 axes play important roles in the regulation of the Th17/Treg balance in ITP
Abstract Immune thrombocytopenia (ITP) is an autoimmune disease caused by T-cell dysfunction. Recently, several studies have shown that a disturbed Th17/Treg balance contributes to the development of ITP. MicroRNAs (miRNAs) are small noncoding RNA moleculesthat posttranscriptionally regulate gene expression. Emerging evidences have demonstrated that miRNAs play an important role in regulating the Th17/Treg balance. In the present study, we found that miR-641 was upregulated in ITP patients. In primary T cells, overexpression of miR-641 could cause downregulation of its target genes STIM1 and SATB1, thus inducing a Th17 (upregulated)/Treg (downregulated) imbalance. Inhibition of miR-641 by a miR-641 sponge in primary T cells of ITP patients or by antagomiR-641 in an ITP murine model could cause upregulation of STIM1 and SATB1, thus restoring Th17/Treg homeostasis. These results suggested that the miR-641-STIM/SATB1 axis plays an important role in regulating the Th17/Treg balance in ITP