Parameter Disentanglement for Diverse Representations

Recent advances in neural network architectures reveal the importance of diverse representations. However, simply integrating more branches or increasing the width for the diversity would inevitably increase model complexity, leading to prohibitive inference costs. In this paper, we revisit the lear...

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Bibliographic Details
Main Authors: Jingxu Wang, Jingda Guo, Ruili Wang, Zhao Zhang, Liyong Fu, Qiaolin Ye
Format: Article
Language:English
Published: Tsinghua University Press 2025-05-01
Series:Big Data Mining and Analytics
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Online Access:https://www.sciopen.com/article/10.26599/BDMA.2024.9020087
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