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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
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双边的敏度意识最小化为平面最小化.

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    此摘要是机器生成的。

    敏度感知最小化 (SAM) 有一个平度指示器问题. 我们建议双边SAM (BSAM) 使用min-sharpness (MinS) 找到更平坦的最小值,提高深度神经网络的概括性和稳定性.

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    科学领域:

    • 深度学习 (Deep Learning) 是一种深度学习.
    • 优化优化 优化优化
    • 计算机视觉 计算机视觉

    背景情况:

    • 敏度感知最小化 (SAM) 通过最小化最大敏度 (MaxS) 来改善深度神经网络 (DNN) 泛化.
    • 萨姆的MaxS度量受到平坦度指标问题 (FIP) 的影响,不充分评估解决方案区域的平坦度,导致高的赫森固有值.
    • 这表明平坦度不足,妨碍了最佳的概括性和强度.

    研究的目的:

    • 为了解决SAM中的平度指示器问题.
    • 开发一种新的平度指标,更好地反映解决方案区域的几何形状.
    • 通过改进的优化来增强DNN的通用化和稳定性.

    主要方法:

    • 引入了min-sharpness (MinS) 作为一个新的平度指标,测量训练和最小邻里损失之间的差异.
    • 拟议的双边SAM (BSAM),将MaxS和MinS集成为更全面的平面性评估.
    • 进行理论分析以证明BSAM的收性质.

    主要成果:

    • 与香草SAM相比,BSAM确定了更平坦的最小值.
    • 在各种任务 (分类,转移学习,姿势估计,语义细分,量化) 上进行了广泛的实验,表明了BSAM的优势.
    • 与SAM相比,BSAM表现出更好的泛化性能和稳定性.

    结论:

    • 拟议的最小度度量有效地解决了SAM的平度指标问题.
    • 双边SAM (BSAM) 为深度神经网络提供了一个更强大,更有效的优化策略.
    • 在广泛的计算机视觉任务中,BSAM实现了卓越的概括性和稳定性.