Transferable regularization and normalization: Towards transferable feature learning for unsupervised domain adaptation

Article Computer Science, Information Systems

Video Domain Adaptation based on Optimal Transport in Grassmann Manifolds

Tianhang Long et al.

Summary: This paper proposes an optimal transport based video domain adaptation model on Grassmann manifolds. It aims to reduce the discrepancy between frame and video level features to improve video adaptation effectiveness.

INFORMATION SCIENCES (2022)

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Article Computer Science, Artificial Intelligence

Brain inspired lifelong learning model based on neural based learning classifier for underwater data classification

Muhammad Irfan et al.

Summary: The study proposed a lifelong learning model that extracts and reuses knowledge to solve current problems, and experimental results show its superior performance in terms of classification accuracy compared to other methods.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Maximum Density Divergence for Domain Adaptation

Jingjing Li et al.

Summary: This paper proposes a new domain adaptation method named ATM, which aims to reduce domain divergence by minimizing inter-domain divergence and maximizing intra-class density, combining adversarial training and metric learning. Experimental results show that the proposed ATM achieves state-of-the-art performance on multiple benchmark datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Computer Science, Information Systems

Deep Discriminative Domain Adaptation

Changchun Zhang et al.

Summary: Domain adaptation focuses on transferring knowledge between different domains, where the Deep Discriminative Domain Adaptation (DDDA) method is proposed to address issues such as deficient knowledge transfer and misclassification. By jointly minimizing losses on different layers with the help of attention mechanism, DDDA significantly enhances the generalization ability of transfer models.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems

A novel lifelong learning model based on cross domain knowledge extraction and transfer to classify underwater images

Muhammad Irfan et al.

Summary: This paper presents a lifelong learning model to solve the challenging problem of real world underwater image classification. The model is capable of continuously learning, accumulating knowledge, and using it to solve future complex problems, similar to human learning process. Experimental results demonstrate that the proposed method outperforms baseline methods and state-of-the-art convolutional neural network (CNN) methods.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

Learning Target-Domain-Specific Classifier for Partial Domain Adaptation

Chuan-Xian Ren et al.

Summary: This article focuses on partial domain adaptation (PDA) and proposes a TSCDA method to address negative transfer by partially aligning feature distributions and learning target-specific classifiers.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Deep Residual Correction Network for Partial Domain Adaptation

Shuang Li et al.

Summary: Deep Residual Correction Network (DRCN) enhances adaptation from source to target by plugging a residual block and task-specific feature layer, while weakening the influence from irrelevant source classes. Weighted class-wise domain alignment loss is designed to couple two domains by matching shared class feature distributions. DRCN outperforms competitive deep domain adaptation approaches in partial, traditional, and fine-grained cross-domain visual recognition experiments.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

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Article Computer Science, Hardware & Architecture