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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

An Improved AdaBoost Method in Imbalanced Data Learning

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An Improved AdaBoost Method in Imbalanced Data Learning

Cyber-Physical Social intelligence (ICCSI), International Conference on

作者： Ting Li Xiaofeng Chen Weikai Li Department of Mathematics Chongqing Jiaotong University Chongqing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Learning from imbalanced datasets has recently received increasing research attention. Despite the remarkable results of AdaBoost in balanced situation, the imbalance problem remains to be solved. To address this, this paper proposes a novel method to improve the AdaBoost using a new error factor and a weighted voting parameter for a few classes and weak classifiers. Our method can weaken the dominant role of the majority class in iterative training by improving the classification ability of the minority class. The superiority of the proposed algorithm is finally proved theoretically and practically. Several real-world imbalance datasets in experiments as specific support, and our method outperforms previous algorithms, especially in the F-1 metric.

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A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

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arXiv 2021年

作者： Wen, Chao Xu, Miao Zhang, Zhilin Zheng, Zhenzhe Wang, Yuhui Liu, Xiangyu Rong, Yu Xie, Dong Tan, Xiaoyang Yu, Chuan Xu, Jian Wu, Fan Chen, Guihai Zhu, Xiaoqiang Zheng, Bo MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Shanghai Jiao Tong University China Alibaba Group China

In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objectives and constraints. Previous works designed auto-bidding tools from the view of single-agent, without modeling the mutual influence between agents. In this paper, we instead consider this problem from a distributed multi-agent perspective, and propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. First, we investigate the competition and cooperation relation among auto-bidding agents, and propose a temperature-regularized credit assignment to establish a mixed cooperative-competitive paradigm. By carefully making a competition and cooperation trade-off among agents, we can reach an equilibrium state that guarantees not only individual advertiser’s utility but also the system performance (i.e., social welfare). Second, to avoid the potential collusion behaviors of bidding low prices underlying the cooperation, we further propose bar agents to set a personalized bidding bar for each agent, and then alleviate the revenue degradation due to the cooperation. Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach. By grouping advertisers with the same objective as a mean auto-bidding agent, the interactions among the large-scale advertisers are greatly simplified, making it practical to train MAAB efficiently. Extensive experiments on the offline industrial dataset and Alibaba advertising platform demonstrate that our approach outperforms several baseline methods in terms of social welfare and revenue. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

Cross-Subject Data Splitting for Brain-to-Text Decoding

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arXiv 2023年

作者： Yin, Congchi Yu, Qian Fang, Zhiwei He, Jie Peng, Changping Lin, Zhangang Shao, Jingping Li, Piji Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China *** Beijing China

Recent major milestones have successfully decoded non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and electroencephalogram (EEG)) into natural language. Despite the progress in model design, how to split the datasets for training, validating, and testing still remains a matter of debate. Most of the prior researches applied subject-specific data splitting, where the decoding model is trained and evaluated per subject. Such splitting method poses challenges to the utilization efficiency of dataset as well as the generalization of models. In this study, we propose a cross-subject data splitting criterion for brain-to-text decoding on various types of cognitive dataset (fMRI, EEG), aiming to maximize dataset utilization and improve model generalization. We undertake a comprehensive analysis on existing cross-subject data splitting strategies and prove that all these methods suffer from data leakage, namely the leakage of test data to training set, which significantly leads to overfitting and overestimation of decoding models. The proposed cross-subject splitting method successfully addresses the data leakage problem and we re-evaluate some SOTA brain-to-text decoding models as baselines for further research. © 2023, CC BY.

关键词： Electroencephalography

Greedy-Step Off-Policy Reinforcement Learning

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arXiv 2021年

作者： Wang, Yuhui Wu, Qingyuan He, Pengcheng Tan, Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and Value Iteration (VI). However, multi-step bootstrapping is often at cross-purposes with and off-policy learning in PI-based methods due to the large variance of multi-step off-policy correction. In contrast, VI-based methods are naturally off-policy but subject to one-step learning. In this paper, we deduce a novel multi-step Bellman Optimality Equation by utilizing a latent structure of multi-step bootstrapping with the optimal value function. Via this new equation, we derive a new multi-step value iteration method that converges to the optimal value function with exponential contraction rate O(γn) but only linear computational complexity. Moreover, it can naturally derive a suite of multi-step off-policy algorithms that can safely utilize data collected by arbitrary policies without correction. Experiments reveal that the proposed methods are reliable, easy to implement and achieve state-of-the-art performance on a series of standard benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends

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arXiv 2023年

作者： Wang, Xinrui Wan, Wenhai Geng, Chuanxin Li, Shaoyuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns. Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to 11.3% in key metrics. The code is available at https://***/wxr99/HolisticPU. © 2023, CC BY.

关键词： Iterative methods

Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning

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arXiv 2023年

作者： Zhao, Ruoqing Wang, Xi Dai, Hongliang Gao, Pan Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Automated radiology report generation has the potential to improve radiology reporting and alleviate the workload of radiologists. However, the medical report generation task poses unique challenges due to the limited availability of medical data and the presence of data bias. To maximize the utility of available data and reduce data bias, we propose MSCL (Medical image Segmentation with Contrastive Learning), a framework that utilizes the Segment Anything Model (SAM) to segment organs, abnormalities, bones, etc., and can pay more attention to the meaningful ROIs in the image to get better visual representations. Then we introduce a supervised contrastive loss that assigns more weight to reports that are semantically similar to the target while training. The design of this loss function aims to mitigate the impact of data bias and encourage the model to capture the essential features of a medical image and generate high-quality reports. Experimental results demonstrate the effectiveness of our proposed model, where we achieve state-of-the-art performance on the IU X-Ray public dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks

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arXiv 2024年

作者： Ni, Xuanfan Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledge, no work has specifically investigated the performance of LLMs in natural language generation (NLG) tasks, a pivotal criterion for determining model excellence. Thus, this paper conducts a comprehensive evaluation of well-known and high-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-based models, and Pythia-based models, in the context of NLG tasks. We select English and Chinese datasets encompassing Dialogue Generation and Text Summarization. Moreover, we propose a common evaluation setting that incorporates input templates and post-processing strategies. Our study reports both automatic results, accompanied by a detailed analysis. © 2024, CC BY.

关键词： Natural language processing systems

A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends

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arXiv 2024年

作者： Li, Xiang Liu, Jiexi Wang, Xinrui Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

关键词： machine learning