Adversarial Learning Method Research Articles

CBIL: Collective Behavior Imitation Learning for Fish from Real Videos

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground-truth motion trajectories and struggle with authenticity, especially in high-density groups with erratic movements. In this paper, we present a scalable approach, Collective Behavior Imitation Learning (CBIL), for learning fish schooling behavior directly from videos , without relying on captured motion trajectories. Our method first leverages Video Representation Learning, in which a Masked Video AutoEncoder (MVAE) extracts implicit states from video inputs in a self-supervised manner. The MVAE effectively maps 2D observations to implicit states that are compact and expressive for following the imitation learning stage. Then, we propose a novel adversarial imitation learning method to effectively capture complex movements of the schools of fish, enabling efficient imitation of the distribution of motion patterns measured in the latent space. It also incorporates bio-inspired rewards alongside priors to regularize and stabilize training. Once trained, CBIL can be used for various animation tasks with the learned collective motion priors. We further show its effectiveness across different species. Finally, we demonstrate the application of our system in detecting abnormal fish behavior from in-the-wild videos.

Мультиагентное тестирование на проникновение на основе AIRL

This paper explores the application of a multi-agent approach based on the Adversarial Inverse Reinforcement Learning (AIRL) method for penetration testing in information systems. Theoretical aspects of multi-agent AIRL are discussed, including the ability to model complex, multi-stage attacks, coordinate agent actions, and learn with partial observability, which accounts for limitations in information access. The practical application of this approach will demonstrate its effectiveness in identifying vulnerabilities, providing a deeper and more accurate security analysis.

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

Unsupervised anomaly detection using inverse generative adversarial networks

Unsupervised anomaly detection (UAD) holds considerable promise for a myriad of real-world applications related to information sciences. Recently, generative models, such as Generative Adversarial Networks (GANs), have shown their potential in addressing UAD tasks. Despite their potential, current GAN-based UAD methods frequently employ intricate network architectures. Some even necessitate solving an iterative optimization problem for each test sample, making them both time-consuming and impractical for large-scale datasets or time-sensitive industrial applications. In this work, we introduce a novel generative adversarial learning method for UAD. Diverging from existing GAN-based methods, our method incorporates a distribution converter and a distribution discriminator. The converter transforms normal data into a compact target distribution such as a truncated Gaussian while the discriminator endeavors to distinguish the transformed data from the data in the target distribution. After an adversarial training process, the learned converter becomes proficient in transforming normal data into the target distribution but cannot accurately transform anomalous samples to the target distribution, which can be easily identified by the strong discriminator. Extensive experiments on nine publicly available anomaly detection benchmark datasets and a real-world, large-scale logistics dataset demonstrate that our method achieves superior detection accuracy and lower inference time than many strong baselines. Our code is publicly available in https://github.com/xiaofeng-github/IGAN.

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable features under specific tasks, which affects the diagnostic performance. Hence, this paper proposes a complementary weighted dual adversarial network combined with supervised contrastive learning (CWDAN-SCL) to address the open-set cross-different working fault diagnosis of bearings. Specifically, a novel complementary weighted adversarial learning strategy is designed using supervised classification and uncertainty measurement to effectively control the participation of target domain features in the domain adaptation process and achieve the alignment of shared class fault features between the source and target domains. Moreover, an adaptive unknown fault separation module is designed using an adversarial learning method to construct a hyperplane between shared and unknown class fault features in the target domain to identify unknown class faults accurately. Additionally, a supervised contrastive loss term is designed based on contrastive learning and label knowledge to improve the aggregation of fault features of the same class and enhance the model’s generalization ability in target domain diagnosis tasks. Subsequently, the efficacy and advancement of the proposed method are substantiated through experimentation on two datasets. The experimental results illustrate that the average diagnostic performance of the proposed method is 91.73 %. This study contributes a dependable diagnostic approach for ascertaining the health status of rotating machinery equipment.

A Novel Adversarial Deep Learning Method for Substation Defect Image Generation.

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgrounds, such as rust and surface oil leakage in substation equipment, which leads to the poor performance of detection models, this paper proposes a novel adversarial deep learning model for substation defect image generation: the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN). Unlike existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model uses a joint discriminator for both overall images and defect images to address the issue of low attention to local defect areas, thereby reducing the loss of image features. This approach enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves a mean average precision (mAP) of 81.5% on the test dataset, and outperforms other image data augmentation and generation methods. This confirms that the ADD-GAN method can generate a high-fidelity image dataset of substation equipment defects.

Inverse Adversarial Diversity Learning for Network Ensemble.

Network ensemble aims to obtain better results by aggregating the predictions of multiple weak networks, in which how to keep the diversity of different networks plays a critical role in the training process. Many existing approaches keep this kind of diversity either by simply using different network initializations or data partitions, which often requires repeated attempts to pursue a relatively high performance. In this article, we propose a novel inverse adversarial diversity learning (IADL) method to learn a simple yet effective ensemble regime, which can be easily implemented in the following two steps. First, we take each weak network as a generator and design a discriminator to judge the difference between the features extracted by different weak networks. Second, we present an inverse adversarial diversity constraint to push the discriminator to cheat generators that all the resulting features of the same image are too similar to distinguish each other. As a result, diverse features will be extracted by these weak networks through a min-max optimization. What is more, our method can be applied to a variety of tasks, such as image classification and image retrieval, by applying a multitask learning objective function to train all these weak networks in an end-to-end manner. We conduct extensive experiments on the CIFAR-10, CIFAR-100, CUB200-2011, and CARS196 datasets, in which the results show that our method significantly outperforms most of the state-of-the-art approaches.

A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction

Long-term remaining useful life (RUL) prediction is essential for the maintenance of safety-crucial engineering assets. Deep learning (DL) models, especially Transformer-based models have achieved outstanding performance in long-term RUL prediction. However, existing Transformer models neglect the impact of discrepancy loss in model training. The accumulation of the discrepancy loss during the inference will hamper the generalization of prediction model, resulting in an overfitting problem. To address the problem, this paper proposes a Bayesian Adversarial Probsparse Transformer (BAPT) model for long-term RUL prediction. Firstly, the adversarial learning method is leveraged to mitigate the impact of accumulated discrepancy loss caused by varying working conditions in long-term prediction, thus diminishing the error accumulation. Secondly, the Probsparse multi-head attention is adopted to enhance the efficiency of feature extraction. The Probsparse multi-head attention focuses on the significant degradation features in long time-series to reduce the computation complexity. Lastly, the Bayesian neural network is introduced to quantify the uncertainty in RUL prediction. The effectiveness of the proposed model is verified using two commercial aircraft turbofan engine datasets. The results indicate that BAPT model for long-term RUL prediction demonstrates better performance than the existing state-of-the-art models.

An adversarial transfer learning method based on domain distribution prediction for aero-engine fault diagnosis

The utilization of transfer learning enables effective realization of the transferability of aero-engine fault diagnosis models across disparate states. However, the gradual degradation of engine performance and the complex and variable flight states lead to continuous changes in data distribution. The common transfer learning methods employ discrete divisions of data domain distributions, which are insufficient to cope with the continuous changes of the domain. To accomplish transfer learning towards a continuous and multi-dimensional target domain, we propose a continuous domain distribution adversarial network (CDDAN) based on deep domain adversarial network. The method defines the number of effective cycles directly associated with engine degradation as a continuous domain index. When the domain index is extended to multiple dimensions, a hybrid gaussian model is introduced to represent distribution prediction within multi-dimensional continuous domains. Subsequently, we validate the feasibility and superiority of our method using datasets generated based on twin-spool turbofan engines. In comparison to other transfer learning methods, the proposed method achieves superior effect in continuous domain adaptation. The experimental results demonstrate that this method exhibits adaptability to failures in the diagnosis method caused by performance degradation and changes in flight state of the aero engine, while remaining independent of target domain data annotation. This advantage becomes particularly apparent in scenarios with limited target domain data and multi-state fault diagnosis.

Balancing Transferability and Discriminability for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to leverage a sufficiently labeled source domain to classify or represent the fully unlabeled target domain with a different distribution. Generally, the existing approaches try to learn a domain-invariant representation for feature transferability and add class discriminability constraints for feature discriminability. However, the feature transferability and discriminability are usually not synchronized, and there are even some contradictions between them, which is often ignored and, thus, reduces the accuracy of recognition. In this brief, we propose a deep multirepresentations adversarial learning (DMAL) method to explore and mitigate the inconsistency between feature transferability and discriminability in UDA task. Specifically, we consider feature representation learning at both the domain level and class level and explore four types of feature representations: domain-invariant, domain-specific, class-invariant, and class-specific. The first two types indicate the transferability of features, and the last two indicate the discriminability. We develop an adversarial learning strategy between the four representations to make the feature transferability and discriminability to be gradually synchronized. A series of experimental results verify that the proposed DMAL achieves comparable and promising results on six UDA datasets.

Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Battery energy storage systems (BESS) play a pivotal role in energy management, and the precise estimation of battery capacity is crucial for optimizing their performance and ensuring reliable power supply. Deep learning methodologies applied to battery capacity estimation have exhibited exemplary performance. However, deep learning methods necessitate supervised training with a significant volume of labeled data, presenting challenges for data collection in industrial scenarios. Moreover, a diverse range of battery types in industrial settings makes it difficult to develop capacity estimation models for different types of batteries from scratch. To address these issues, a semi-supervised adversarial deep learning (SADL) method is proposed for lithium-ion battery capacity estimation. Initially, a subset of labeled lithium-ion battery data, coupled with a subset of unlabeled data, is collected. Voltage and current data are then transformed into capacity increment features. Subsequently, an adversarial training strategy is employed, subjecting labeled and unlabeled data to adversarial training to enhance the performance of SADL. Finally, the effectiveness of the SADL method in estimating the capacity of other lithium-ion batteries is analysed. Experimental results demonstrate that the SADL method accurately estimates the capacity of various battery types, showcasing an RMSE error of approximately 2%, surpassing the performance of other methods. The proposed SADL method emerges as a promising solution for the precise estimation of lithium-ion battery capacity in BESS.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Adaptive proposal network based on generative adversarial learning for weakly supervised temporal sentence grounding

Temporal sentence grounding aims to locate the moment most related to the given natural language query. Noticing the time-consuming labeling process of the temporal bounding boxes, recent works started to focus on the weakly supervised temporal sentence grounding (WTSG) with only video-text pairwise annotations. Existing WTSG methods mainly adopted anchor-based structure to generate moment candidates and trained the network with triplet loss between the positive and negative samples, and thus the network performance was seriously affected by the preset anchors and the loss margin. In this paper, we propose a novel contrastive generative adversarial learning method with adaptive box generation for WTSG tasks. Specifically, the temporal proposals are adaptively generated by a transformer-based box generator in a complete anchor-free manner. And a novel contrastive generative adversarial learning process is proposed for the network optimization, which can effectively encourages the separation of the positive and negative samples without preset margin value. Extensive experiments indicate that our method achieves the state-of-the-art performance on both of the Charades-STA and the ActivityNet Captions datasets.

Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification

Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject’s physiological attributes. The progress of deep learning and advanced domain generalization methods allows automatic and even adaptive sleep stage classification. However, applying models trained with visible subject data to invisible subject data remains challenging due to significant individual differences among subjects. Motivated by the periodic category-complete structure of sleep stage classification, we propose a Structure Incentive Domain Adversarial learning (SIDA) method that combines the sleep stage classification method with domain generalization to enable cross-subject sleep stage classification. SIDA includes individual domain discriminators for each sleep stage category to decouple subject dependence differences among different categories and fine-grained learning of domain-invariant features. Furthermore, SIDA directly connects the label classifier and domain discriminators to promote the training process. Experiments on three benchmark sleep stage classification datasets demonstrate that the proposed SIDA method outperforms other state-of-the-art sleep stage classification and domain generalization methods and achieves the best cross-subject sleep stage classification results.

Adversarial Active Learning Method for Enhanced Object Detection Guided by Uncertainty

Adversarial Active Learning Method for Enhanced Object Detection Guided by Uncertainty

Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment

Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, annotating aesthetic attributes is a time-consuming, costly, and error-prone task, which leads to the issue that photos available are partially annotated with attributes. To alleviate this issue, we propose a novel semi-supervised adversarial learning method for photo aesthetic assessment from partially attribute-annotated photos, which can greatly reduce the reliance on manual attribute annotation. Specifically, the proposed method consists of a score-attributes generator R, a photo generator G, and a discriminator D. The score-attributes generator learns the aesthetic score and attributes simultaneously to capture their dependencies and construct better feature representations. The photo generator reconstructs the photo by feeding aesthetic attributes, score, and informative feature representation. A discriminator is used to force the convergence of the features-attributes-score tuples generated from the score-attributes generator, the photo generator, and the ground-truth distribution in labeled data for training data. The proposed method significantly outperforms the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) from the existing best reported of 0.726 to 0.761 on Aesthetic and attributes database and 0.756 to 0.774 on Aesthetic visual analysis database, respectively.

Deep Reinforcement Learning for Nash Equilibrium of Differential Games.

Nash equilibrium is a significant solution concept representing the optimal strategy in an uncooperative multiagent system. This study presents two deep reinforcement learning (DRL) algorithms for solving the Nash equilibrium of differential games. Both algorithms are built upon the distributed distributional deep deterministic policy gradient (D4PG) algorithm, which is a one-sided learning method. We modified it to a two-sided adversarial learning method. The first is D4PG for games (D4P2G), which directly applies an adversarial play framework based on the D4PG. A simultaneous policy gradient descent (SPGD) method is employed to optimize the policies of the players with conflicting objectives. The second is the distributional deep deterministic symplectic policy gradient (D4SPG) algorithm, which is our main contribution. More specifically, it newly designs a minimax learning framework that combines the critics of the two players and proposes a symplectic policy gradient adjustment method to find a better policy gradient. Simulations show that both algorithms converge to the Nash equilibrium in most cases, but D4SPG can learn the Nash equilibrium more accurately and efficiently, especially in Hamiltonian games. Moreover, it can handle games with complex dynamics, which is challenging for traditional methods.

A Joint Batch Correction and Adaptive Clustering Method of Single-Cell Transcriptomic Data

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with Adversarial Learning method (DACAL) is proposed here. DACAL jointly optimizes the batch correcting and clustering processes to remove batch effects while retaining biological information. DACAL achieves batch correction and adaptive clustering without requiring manually specified cell types or resolution parameters. DACAL is compared with other widely used batch correction and clustering methods on human pancreas datasets from different sequencing platforms and mouse mammary datasets from different laboratories. The results demonstrate that DACAL can correct batch effects efficiently and adaptively find accurate cell types, outperforming competing methods. Moreover, it can obtain cell subtypes with biological meanings.

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.

Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning. Although some existing works have proposed methods for sampling the best examples for annotation in medical image analysis, they are not task-agnostic and do not use multimodal auxiliary information in the sampler, which has the potential to increase robustness. Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our method to two datasets: i) brain tumor segmentation and multi-label classification using the BraTS2018 dataset, and ii) chest X-ray image classification using the COVID-QU-Ex dataset. Our results show a promising direction toward data-efficient learning under limited annotations.

Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance.

Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which limit the application and development of deep learning techniques, and thus it is imperative to study unsupervised machine learning. The learning of deep representations by mutual information estimation and maximization (Deep InfoMax or DIM) method has achieved unprecedented results in the field of unsupervised learning. However, in the DIM method, to restrict the encoder to learn more normalized feature representations, an adversarial network learning method is used to make the encoder output consistent with a priori positively distributed data. As we know, the model training of the adversarial network learning method is difficult to converge, because there is a logarithmic function in the loss function of the cross-entropy measure, and the gradient of the model parameters is susceptible to the "gradient explosion" or "gradient disappearance" phenomena, which makes the training of the DIM method extremely unstable. In this regard, we propose a Wasserstein distance-based DIM method to solve the stability problem of model training, and our method is called the WDIM. Subsequently, the training stability of the WDIM method and the classification ability of unsupervised learning are verified on the CIFAR10, CIFAR100, and STL10 datasets. The experiments show that our proposed WDIM method is more stable to parameter updates, has faster model convergence, and at the same time, has almost the same accuracy as the DIM method on the classification task of unsupervised learning. Finally, we also propose a reflection of future research for the WDIM method, aiming to provide a research idea and direction for solving the image classification task with unsupervised learning.