Related references
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Article
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Jingjing Li et al.
Summary: This paper discusses the generalization problem of conventional machine learning algorithms when dealing with data from different distributions and proposes an unsupervised domain adaptation method. The authors consider the scenario where either the source domain data or the target domain data is unknown, and investigate how to handle the divergence-agnostic adaptive learning problem from the perspective of adversarial attacks. Experimental results show that the proposed method outperforms previous ones.
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Proceedings Paper
Computer Science, Artificial Intelligence
Fan Wang et al.
Summary: Source free domain adaptation (SFDA) is a critical method to transfer knowledge from a well-trained source model to an unlabeled target domain. Most existing methods focus on learning target-specific domain-invariant representations, which cannot fully address the distribution shift problem. In contrast, exploring the domain-invariant parameters of the source model is a more effective approach.
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(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Lin Chen et al.
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Proceedings Paper
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Summary: This work introduces a new framework for source-free domain adaptation, addressing the domain adaptation task through source distribution estimation. By generating robust pseudo-labels for target data with spherical k-means clustering, estimating the class-conditioned feature distribution of the source domain, and sampling surrogate features to align the two domains, the proposed method achieves state-of-the-art performance on multiple DA benchmarks.
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Proceedings Paper
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Proceedings Paper
Computer Science, Artificial Intelligence
Haifeng Xia et al.
Summary: The study proposes an Adaptive Adversarial Network (A2Net) with three components to address the challenge of source data unavailability in the Source-free Domain Adaptation scenario. The components include Adaptive Adversarial Inference, Contrastive Category-wise Matching module, and Self-Supervised Rotation, which collectively help improve the adaptability of the model without needing access to the source domain data.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
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Proceedings Paper
Computer Science, Artificial Intelligence
Shiqi Yang et al.
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2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
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Article
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Abdollah Amirkhani et al.
Summary: Recent studies have utilized knowledge distillation (KD) technique to address time-consuming annotation task in semantic segmentation. This study aims to train a robust, lightweight student under the supervision of several expert teachers, leading to better performance compared to a single-teacher learning framework. The proposed multi-teacher KD paradigm allows the student to capture a variety of knowledge illustrations from different sources, resulting in significant improvement in both clean and corrupted data in the semantic segmentation task.
