The paper investigates whether strong generalization on a source task guarantees transferability to a related target task in transfer learning. The authors hypothesize a negative answer and conduct adversarial attacks on the transfer learning process to find counterexamples. Their empirical results reveal a strong relationship between source-task generalization and transferability, though they do not rule out the possibility that good source generalization is sufficient for successful transfer.
By Mina Remeli
The paper introduces Transfer Learning for Evolving Domains (TrED), a framework that models how data availability changes over time in real-world applications. TrED treats the entire trajectory of model updates as a single learning problem, rather than isolated snapshots, and defines a data availability process, a flexible learning protocol, and an evaluation criterion that scores the whole trajectory. The authors review existing transfer learning methods, noting that most are tailored to specific regimes and do not optimize the full trajectory, and argue that TrED is a well‑posed, unsolved research direction.
By Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, Pedro Ribeiro, Pedro Saleiro, Pedro Bizarro, Carlos Soares
arXiv:2408.16189v4 Announce Type: replace-cross
Abstract: Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution P to im...
By Steve Hanneke, Samory Kpotufe
arXiv:2507.02248v2 Announce Type: replace-cross
Abstract: In this paper, we explore the knowledge transfer under the setting of matrix completion, which aims to enhance the estimation of a low-rank t...
By Dali Liu, Yuying Xie, Haolei Weng
arXiv:2412. 18081v3 Announce Type: replace-cross Abstract: We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets.
By Jae Ho Chang, Massimiliano Russo, Subhadeep Paul
arXiv:2502.06970v3 Announce Type: replace
Abstract: In contemporary deep learning, a prevalent and effective workflow for solving low-data problems is adapting powerful pre-trained foundation models...
By Fady Rezk, Royson Lee, Henry Gouk, Timothy Hospedales, Minyoung Kim
arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.
By B\'er\'enice-Alexia Jocteur (ICJ, PSPM), V\'eronique Maume-Deschamps (ICJ, PSPM), Pierre Ribereau (PSPM, ICJ)
arXiv:2607. 03005v1 Announce Type: new Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging.
By Joonho Kim, Seyoung Park
The paper introduces a model‑agnostic, nonparametric framework for classification when training data contains label noise, particularly useful when a large noisy dataset is supplemented by a small clean dataset. It addresses the distribution shift between noisy and clean data that undermines traditional parametric transfer learning methods, and it is applicable to a wide range of classifiers. The authors provide a rigorous statistical theory and validate the approach with simulations and a real‑world medical imaging study on pneumonia diagnosis.
By Zhu Guojun, Zhang Sanguo, Ren Mingyang
arXiv:2606. 00558v1 Announce Type: new Abstract: Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain.
By Yuan Yao, Jin Song, Huixia Li, Tongtong Yuan, Jiaqi Wu, Yu Zhang
The paper introduces CATTLE, a transfer learning framework for disjoint tabular datasets that eliminates the need for shared features by leveraging generalized context learned through transformer projection weights. By using key, value, and query weights from source and target domains, CATTLE performs cross‑domain attention transfer in a data‑agnostic manner. Experiments on ten source‑target pairs demonstrate that CATTLE outperforms nine state‑of‑the‑art baselines, achieving the best average rank (2.9) and a 3.7% AUROC improvement.
By Kazi F. Akhter, Ibna Kowsar, Manar D. Samad
arXiv:2606. 18567v1 Announce Type: cross Abstract: This paper presents a methodology-centered transfer learning framework for fragility adaptation under domain shift, class imbalance, and scarce target labels while preserving engineering interpretability and supporting decision-making under uncertainty.
By Narges Saeednejad, Jamie Ellen Padgett