arXiv AI June 18, 2026 By Masashi Noguchi, Shinichi Shirakawa

Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization

arXiv:2303. 18031v2 Announce Type: replace-cross Abstract: In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios

arXiv:2606. 23758v1 Announce Type: cross Abstract: Domain generalization learns from multiple source domains to generalize to unseen target domains.

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arXiv AI

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BPG: Balancing Plasticity and Generalization for Domain Incremental Learning

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Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets

Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets remain a challenge, especially when models are applied to unseen domains.

llms fine-tuning

arXiv Machine Learning

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DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.

By Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang

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UME: A Unified Meta-Generalization Framework for Cross-Domain ETA

arXiv:2606. 00979v1 Announce Type: new Abstract: Accurate Estimated Time of Arrival (ETA) prediction on checkout page is crucial in instant logistics for enhancing user satisfaction, optimizing dispatching, and controlling operational costs.

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Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.

By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

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Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios

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Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

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