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Transfer Learning for Evolving Domains

Paper recorded by Signals 4 on 2026-09-11 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.LG · 机器学习 · first seen 2026-09-14

Abstract

Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets). Typically, transfer learning research is segmented into several isolated sub-areas (such as domain generalisation, domain adaptation, or multi-domain learning), each making distinct assumptions about target data availability, namely h

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#63 most recent of 215 cs.LG papers we have recorded · ↑ newer: Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient D · ↓ older: Dual-guided Hierarchical Edge Localization for Large-scale Optimal Tra
Cite this page: Transfer Learning for Evolving Domains: the #63 most recent of 215 cs.LG papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/transfer-learning-for-evolving-domains.html
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