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Xinge You

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10 papers

2 author rows

EAAI Journal 2024 Journal Article

Concept drift adaptation with scarce labels: A novel approach based on diffusion and adversarial learning

Liheng Yuan
Fanghua Ye
Wei Zhou
Wei Yuan
Xinge You

The distribution of streaming data may change over time, making the knowledge previously learned by machine learning models outdated. This phenomenon is known as concept drift. It is common in many fields, such as weather forecasting, electricity prediction, and computer vision. To address this issue, concept drift adaptation is conducted by updating the parameters or adjusting the architecture of models. However, it may involve changing complex model structures and lead to slow convergence. This challenge is further exacerbated in scenarios with scarce labels, where instance labels are difficult to obtain and only a small amount of labeled data are available. The key to overcoming these challenges is to learn the good feature representations for incoming instances. Accordingly, we propose a novel concept drift adaptation method called CDAL, which incorporates diffusion learning into an adversarial network structure to polish up feature extraction under scarce labels. By simulating a graph-based diffusion process, diffusion learning enhances feature correlation among similar instances and weakens the feature correlation among different instances. On the other hand, adversarial learning enhances the feature extractor by guiding the latter to compete with a pair of specially designed classifiers. With these two techniques, CDAL can facilitate concept drift adaptation and ensure robust performance in scenarios with scarce labels. Additionally, our model exhibits strong performance despite its simple structure, enabling rapid adaptation in the presence of concept drift. We compared our method with four existing state-of-the-art methods on seven public datasets, including synthetic and real-world datasets. The results show that our method achieves superior performance across all datasets.

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ICML Conference 2024 Conference Paper

Optimal Kernel Choice for Score Function-based Causal Discovery

Wenjie Wang
Biwei Huang
Feng Liu 0003
Xinge You
Tongliang Liu
Kun Zhang 0001
Mingming Gong

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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EAAI Journal 2024 Journal Article

Semi-supervised anomaly detection with contamination-resilience and incremental training

Liheng Yuan
Fanghua Ye
Heng Li
Chenhao Zhang
Cuiying Gao
Chengqing Yu
Wei Yuan
Xinge You

Anomaly detection plays a vital role in various realistic applications, including fraud detection, network traffic analysis, medical diagnosis, and so on. Semi-supervised anomaly detection methods have recently attracted increasing attention, owing to their low requirement for labeled anomalous samples. However, existing semi-supervised methods suffer from performance degradation when training data are contaminated with anomalies, and cannot well support incremental training required in scenarios where original training data are hard to obtain. To overcome these limitations, we propose SAE-CRIT, a lightweight semi-supervised anomaly detection method with contamination resilience and incremental training. SAE-CRIT effectively mitigates the negative impact of contaminated data through differentially weighting samples, and leverages a three-layer neural network to detect anomalies, allowing for efficient incremental training by updating only the last layer with new data. We compare SAE-CRIT with eight anomaly detection methods over four datasets. Extensive experiments demonstrate the advantages of SAE-CRIT in contamination resistance, incremental training, and training costs. More specifically, the state-of-the-art detection method GOAD achieved an F1-score of 89. 3% and 90. 6% on the contaminated datasets KDDCUP and KDDCUP-Rev, respectively. Under the same settings, however, SAE-CRIT exhibited an F1-score of 92. 4% and 96. 9%, respectively. In addition, the training time of SAE-CRIT is less than 20 s on these two datasets. The time spent by SAE-CRIT on these two datasets only accounts for 0. 26% and 1. 8% of the total time spent by GOAD, respectively.

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ICML Conference 2023 Conference Paper

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

Shiming Chen 0002
Wenjin Hou
Ziming Hong
Xiaohan Ding
Yibing Song
Xinge You
Tongliang Liu
Kun Zhang 0001

In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing unseen class sample features for better training the classifier. We observe that each class’s predefined semantic prototype (also referred to as semantic embedding or condition) does not accurately match its real semantic prototype. So the synthesized visual sample features do not faithfully represent the real sample features, limiting the classifier training and existing ZSL performance. In this paper, we formulate this mismatch phenomenon as the visual-semantic domain shift problem. We propose a dynamic semantic prototype evolving (DSP) method to align the empirically predefined semantic prototypes and the real prototypes for class-related feature synthesis. The alignment is learned by refining sample features and semantic prototypes in a unified framework and making the synthesized visual sample features approach real sample features. After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5%, 8. 0%, and 9. 7% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.

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IJCAI Conference 2022 Conference Paper

Recent Advances in Concept Drift Adaptation Methods for Deep Learning

Liheng Yuan
Heng Li
Beihao Xia
Cuiying Gao
Mingyue Liu
Wei Yuan
Xinge You

In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e. g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i. e. , model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.

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IJCAI Conference 2022 Conference Paper

Semantic Compression Embedding for Generative Zero-Shot Learning

Ziming Hong
Shiming Chen
Guo-Sen Xie
Wenhan Yang
Jian Zhao
Yuanjie Shao
Qinmu Peng
Xinge You

Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i. e. , CUB, SUN and AWA2, demonstrate the signiﬁcant performance gains of SC-EGG over current state-of-the-art methods and its baselines.

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AAAI Conference 2022 Conference Paper

TransZero: Attribute-Guided Transformer for Zero-Shot Learning

Shiming Chen
Ziming Hong
Yang Liu
Guo-Sen Xie
Baigui Sun
Hao Li
Qinmu Peng
Ke Lu

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region features, enabling significant visual-semantic interaction. Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network, termed TransZero, to refine visual features and learn attribute localization for discriminative visual embedding representations in ZSL. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks, and improves the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the image regions most relevant to each attribute in a given image, under the guidance of semantic attribute information. Then, the locality-augmented visual features and semantic vectors are used to conduct effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves the new state of the art on three ZSL benchmarks. The codes are available at: https: //github. com/shiming-chen/TransZero.

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NeurIPS Conference 2021 Conference Paper

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

Shiming Chen
Guosen Xie
Yang Liu
Qinmu Peng
Baigui Sun
Hao Li
Xinge You
Ling Shao

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and semantic domains in ZSL. However, existing common space learning methods align the semantic and visual domains by merely mitigating distribution disagreement through one-step adaptation. This strategy is usually ineffective due to the heterogeneous nature of the feature representations in the two domains, which intrinsically contain both distribution and structure variations. To address this and advance ZSL, we propose a novel hierarchical semantic-visual adaptation (HSVA) framework. Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e. , structure adaptation and distribution adaptation. In the structure adaptation step, we take two task-specific encoders to encode the source data (visual domain) and the target data (semantic domain) into a structure-aligned common space. To this end, a supervised adversarial discrepancy (SAD) module is proposed to adversarially minimize the discrepancy between the predictions of two task-specific classifiers, thus making the visual and semantic feature manifolds more closely aligned. In the distribution adaptation step, we directly minimize the Wasserstein distance between the latent multivariate Gaussian distributions to align the visual and semantic distributions using a common encoder. Finally, the structure and distribution adaptation are derived in a unified framework under two partially-aligned variational autoencoders. Extensive experiments on four benchmark datasets demonstrate that HSVA achieves superior performance on both conventional and generalized ZSL. The code is available at \url{https: //github. com/shiming-chen/HSVA}.

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IJCAI Conference 2021 Conference Paper

Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval

Wenjie Wang
Yufeng Shi
Shiming Chen
Qinmu Peng
Feng Zheng
Xinge You

Zero-shot sketch-based image retrieval (ZS-SBIR), which aims to retrieve photos with sketches under the zero-shot scenario, has shown extraordinary talents in real-world applications. Most existing methods leverage language models to generate class-prototypes and use them to arrange the locations of all categories in the common space for photos and sketches. Although great progress has been made, few of them consider whether such pre-defined prototypes are necessary for ZS-SBIR, where locations of unseen class samples in the embedding space are actually determined by visual appearance and a visual embedding actually performs better. To this end, we propose a novel Norm-guided Adaptive Visual Embedding (NAVE) model, for adaptively building the common space based on visual similarity instead of language-based pre-defined prototypes. To further enhance the representation quality of unseen classes for both photo and sketch modality, modality norm discrepancy and noisy label regularizer are jointly employed to measure and repair the modality bias of the learned common embedding. Experiments on two challenging datasets demonstrate the superiority of our NAVE over state-of-the-art competitors.

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IJCAI Conference 2019 Conference Paper

Equally-Guided Discriminative Hashing for Cross-modal Retrieval

Yufeng Shi
Xinge You
Feng Zheng
Shuo Wang
Qinmu Peng

Cross-modal hashing intends to project data from two modalities into a common hamming space to perform cross-modal retrieval efficiently. Despite satisfactory performance achieved on real applications, existing methods are incapable of effectively preserving semantic structure to maintain inter-class relationship and improving discriminability to make intra-class samples aggregated simultaneously, which thus limits the higher retrieval performance. To handle this problem, we propose Equally-Guided Discriminative Hashing (EGDH), which jointly takes into consideration semantic structure and discriminability. Specifically, we discover the connection between semantic structure preserving and discriminative methods. Based on it, we directly encode multi-label annotations that act as high-level semantic features to build a common semantic structure preserving classifier. With the common classifier to guide the learning of different modal hash functions equally, hash codes of samples are intra-class aggregated and inter-class relationship preserving. Experimental results on two benchmark datasets demonstrate the superiority of EGDH compared with the state-of-the-arts.

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