scholarly journals Adversarial-Learned Loss for Domain Adaptation

2020 ◽  
Vol 34 (04) ◽  
pp. 3521-3528
Author(s):  
Minghao Chen ◽  
Shuai Zhao ◽  
Haifeng Liu ◽  
Deng Cai
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Confusion Matrix ◽  
Ground Truth ◽  
Adversarial Learning ◽  
Label Method ◽  
Model Predictions ◽  
Novel Method ◽  
Remarkable Progress ◽  
Made In

Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-adversarial learning and self-training. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. On the other hand, self-training utilizes the model predictions to enhance the discrimination of target features, but it is unable to explicitly align domain distributions. In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA). We first analyze the pseudo-label method, a typical self-training method. Nevertheless, there is a gap between pseudo-labels and the ground truth, which can cause incorrect training. Thus we introduce the confusion matrix, which is learned through an adversarial manner in ALDA, to reduce the gap and align the feature distributions. Finally, a new loss function is auto-constructed from the learned confusion matrix, which serves as the loss for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches in four standard domain adaptation datasets. Our code is available at https://github.com/ZJULearning/ALDA.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

Synthesis and applications of CdSe nano-tetrapods in hybrid photovoltaic devices

2012 ◽  
Vol 84 (12) ◽  
pp. 2549-2558 ◽  
Author(s):  
Lijuan Zhao ◽  
Linfeng Hu
Keyword(s):  
State Of The Art ◽  
Chloroform Solution ◽  
Photovoltaic Devices ◽  
Direct Band Gap ◽  
Field Emitters ◽  
Current State ◽  
Photovoltaic Applications ◽  
Direct Band ◽  
Remarkable Progress ◽  
Made In

Tetrapod-shaped nanocrystals have attracted increasing interest for optoelectronic applications in recent years due to their rich morphologies. With unique properties such as a direct band-gap and excellent photoelectrical characteristics, CdSe nano-tetrapods are promising nanostructures for applications in such fields as photodetectors, field emitters, and photovoltaic devices. This review mainly describes the remarkable progress made in synthesis and hybrid photovoltaic applications of CdSe nano-tetrapods over the last few years. In particular, the “blinking” effect observed from these nano-tetrapods in chloroform solution is highlighted. This overview covers the current state of the art as well as an outlook on possibilities and limitations.

scholarly journals Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 44
Author(s):  
Ting Xiao ◽  
Cangning Fan ◽  
Peng Liu ◽  
Hongwei Liu
Keyword(s):  
Conditional Distribution ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Feature Space ◽  
Low Rank ◽  
Computationally Efficient ◽  
Adversarial Learning ◽  
Joint Distributions ◽  
Distribution Matching ◽  
Maximum Separation

Although adversarial domain adaptation enhances feature transferability, the feature discriminability will be degraded in the process of adversarial learning. Moreover, most domain adaptation methods only focus on distribution matching in the feature space; however, shifts in the joint distributions of input features and output labels linger in the network, and thus, the transferability is not fully exploited. In this paper, we propose a matrix rank embedding (MRE) method to enhance feature discriminability and transferability simultaneously. MRE restores a low-rank structure for data in the same class and enforces a maximum separation structure for data in different classes. In this manner, the variations within the subspace are reduced, and the separation between the subspaces is increased, resulting in improved discriminability. In addition to statistically aligning the class-conditional distribution in the feature space, MRE forces the data of the same class in different domains to exhibit an approximate low-rank structure, thereby aligning the class-conditional distribution in the label space, resulting in improved transferability. MRE is computationally efficient and can be used as a plug-and-play term for other adversarial domain adaptation networks. Comprehensive experiments demonstrate that MRE can advance state-of-the-art domain adaptation methods.

Interpreting Model Predictions with Constrained Perturbation and Counterfactual Instances

2021 ◽  
Author(s):  
Jun-Peng Fang ◽  
Jun Zhou ◽  
Qing Cui ◽  
Cai-Zhi Tang ◽  
Long-Fei Li
Keyword(s):  
Financial Markets ◽  
State Of The Art ◽  
Industrial Applications ◽  
Importance Value ◽  
Art Interpretation ◽  
Critical Areas ◽  
Black Boxes ◽  
Model Predictions ◽  
Interpretation Method ◽  
Made In

In recent years, machine learning models have achieved magnificent success in many industrial applications, but most of them are black boxes. It is crucial to understand why such predictions are made in many critical areas such as medicine, financial markets, and auto driving. In this paper, we propose Coco, a novel interpretation method which can interpret any binary classifier by assigning each feature an importance value for a particular prediction. We first adopt MixUp method to generate reasonable perturbations, then apply these perturbations with constraints to obtain counterfactual instances and finally compute a comprehensive metric on these instances to estimate the importance of each feature. To demonstrate the effectiveness of Coco, we conduct extensive experiments on several datasets. The results show our method achieves better performance in identifying the most important features compared with the state-of-the-art interpretation methods, including Shap and Lime.

scholarly journals Image Aesthetic Assessment Assisted by Attributes through Adversarial Learning

2019 ◽  
Vol 33 ◽  
pp. 679-686 ◽  
Author(s):  
Bowen Pan ◽  
Shangfei Wang ◽  
Qisheng Jiang
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Training Data ◽  
Privileged Information ◽  
Adversarial Learning ◽  
Deep Network ◽  
Feature Representations ◽  
The Aesthetic ◽  
Label Distribution ◽  
Representation Level

The inherent connections among aesthetic attributes and aesthetics are crucial for image aesthetic assessment, but have not been thoroughly explored yet. In this paper, we propose a novel image aesthetic assessment assisted by attributes through both representation-level and label-level. The attributes are used as privileged information, which is only required during training. Specifically, we first propose a multitask deep convolutional rating network to learn the aesthetic score and attributes simultaneously. The attributes are explored to construct better feature representations for aesthetic assessment through multi-task learning. After that, we introduce a discriminator to distinguish the predicted attributes and aesthetics of the multi-task deep network from the ground truth label distribution embedded in the training data. The multi-task deep network wants to output aesthetic score and attributes as close to the ground truth labels as possible. Thus the deep network and the discriminator compete with each other. Through adversarial learning, the attributes are explored to enforce the distribution of the predicted attributes and aesthetics to converge to the ground truth label distribution. Experimental results on two benchmark databases demonstrate the superiority of the proposed method to state of the art work.

scholarly journals Multi-EPL: Accurate multi-source domain adaptation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255754
Author(s):  
Seongmin Lee ◽  
Hyunsik Jeon ◽  
U. Kang
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Multiple Source ◽  
Conditional Distributions ◽  
Source Domain ◽  
Multiple Feature ◽  
Crucial Problem ◽  
Novel Method ◽  
Privacy Issues ◽  
Target Data

Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering labels of the features of each domain. They also do not fully utilize the target data without labels and rely on limited feature extraction with a single extractor. In this paper, we propose Multi-EPL, a novel method for MSDA. Multi-EPL exploits label-wise moment matching to align the conditional distributions of the features for the labels, uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that Multi-EPL provides the state-of-the-art performance for MSDA tasks in both image domains and text domains, improving the accuracy by up to 13.20%.

scholarly journals OVL: One-View Learning for Human Retrieval

2020 ◽  
Vol 34 (07) ◽  
pp. 11410-11417
Author(s):  
Wenjing Li ◽  
Zhongcheng Wu
Keyword(s):  
Large Scale ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Set Domain ◽  
Adversarial Learning ◽  
Learning Framework ◽  
Training Images ◽  
Open Set ◽  
Camera View ◽  
Supervised Methods

This paper considers a novel problem, named One-View Learning (OVL), in human retrieval a.k.a. person re-identification (re-ID). Unlike fully-supervised learning, OVL only requires pretty cheap annotation cost: labeled training images are only provided from one camera view (source view/domain), while the annotations of training images from other camera views (target views/domains) are not available. OVL is a problem of multi-target open set domain adaptation that is difficult for existing domain adaptation methods to handle. This is because 1) unlabeled samples are drawn from multiple target views in different distributions, and 2) the target views may contain samples of “unknown identity” that are not shared by the source view. To address this problem, this work introduces a novel one-view learning framework for person re-ID. This is achieved by adversarial multi-view learning (AMVL) and adversarial unknown rejection learning (AURL). The former learns a multi-view discriminator by adversarial learning to align the feature distributions between all views. The later is designed to reject unknown samples from target views through adversarial learning with two unknown identity classifiers. Extensive experiments on three large-scale datasets demonstrate the advantage of the proposed method over state-of-the-art domain adaptation and semi-supervised methods.

scholarly journals Reinforcement Learning with Perturbed Rewards

2020 ◽  
Vol 34 (04) ◽  
pp. 6202-6209 ◽  
Author(s):  
Jingkang Wang ◽  
Yang Liu ◽  
Bo Li
Keyword(s):  
Reinforcement Learning ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Confusion Matrix ◽  
Error Rates ◽  
Average Score ◽  
Sample Complexity ◽  
Noisy Environments ◽  
The Core ◽  
Made In

Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in practice (e.g., when rewards are collected through sensors), and is therefore not credible. In addition, for applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors by receiving corrupted rewards. In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix. We develop a robust RL framework that enables agents to learn in noisy environments where only perturbed rewards are observed. Our solution framework builds on existing RL/DRL algorithms and firstly addresses the biased noisy reward setting without any assumptions on the true distribution (e.g., zero-mean Gaussian noise as made in previous works). The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that trained policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 84.6% and 80.8% improvements on average score for five Atari games, with error rates as 10% and 30% respectively.

scholarly journals DANE: Domain Adaptive Network Embedding

2019 ◽  
Author(s):  
Yizhou Zhang ◽  
Guojie Song ◽  
Lun Du ◽  
Shuwen Yang ◽  
Yilun Jin
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Structured Data ◽  
Network Embedding ◽  
Adaptive Network ◽  
Convolutional Network ◽  
Adversarial Learning ◽  
Multiple Networks ◽  
Shared Set ◽  
Machine Learning Models

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.

scholarly journals Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6182
Author(s):  
Joongchol Shin ◽  
Joonki Paik
Keyword(s):  
Light Transmission ◽  
State Of The Art ◽  
Ground Truth ◽  
Model Estimation ◽  
Learning Method ◽  
Image Dehazing ◽  
Adversarial Learning ◽  
Realistic Image ◽  
Very High ◽  
High Computational Complexity

Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.

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