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arXiv AI By Chenlei Fang, Jingchen Li, Hongzong LI, Qingyao Li, Yixuan Zhang, Huarui Wu, Haobin Shi, Chunjiang Zhao

EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning

Read the original on arXiv AI →

The paper introduces EMAN, an optimization-driven framework that allows a multi‑task network to grow a second independent path only when persistent optimization evidence justifies it. EMAN exposes an antisymmetric growth direction through latent relative phases and monitors multiple decision signals during training to convert local optimization evidence into a structural decision. The resulting architecture allocates shared and task‑specific capacity adaptively, and experiments on controlled rank settings, PASCAL‑Context, and NYUv2 demonstrate improved performance at a competitive computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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