HyperSpaceX：超球面维度的径向与角度探索
HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions
AI总结：
HyperSpaceX 通过 DistArc 损失在多超球面空间中同时探索角度与径向特征，增强类间分离和类内聚合，在物体分类和人脸识别任务上取得最先进结果，低维大规模数据集性能提升最高达 20%。
摘要：
传统深度学习模型依赖 softmax 交叉熵和 ArcFace 损失等方法来完成分类和人脸识别等任务。这些方法主要在超球面空间中探索角度特征，由于大量类别间的角度数据密集，往往导致类间特征相互纠缠。本文提出了一个名为 HyperSpaceX 的新特征探索领域，它通过一种新颖的 DistArc 损失，在多超球面空间中同时探索角度维度和径向维度，从而增强类别判别能力。所提出的 DistArc 损失包含三个特征排布组件：两个角度组件和一个径向组件，在多径向排布中强制实现类内聚合和类间分离，提升特征可判别性。对 HyperSpaceX 框架进行新表示评估时，采用了一种同时考虑角度和径向元素的预测度量，从而在标准指标之外提供更全面的模型精度评估。在七个物体分类数据集和六个人脸识别数据集上的实验表明，HyperSpaceX 取得了最先进的（SoTA）结果，在低维大规模物体数据集上性能提升最高可达 20%，在高维情况下提升最高可达 6%。
英文摘要：
Traditional deep learning models rely on methods such as softmax cross-entropy and ArcFace loss for tasks like classification and face recognition. These methods mainly explore angular features in a hyperspherical space, often resulting in entangled inter-class features due to dense angular data across many classes. In this paper, a new field of feature exploration is proposed known as HyperSpaceX which enhances class discrimination by exploring both angular and radial dimensions in multi-hyperspherical spaces, facilitated by a novel DistArc loss. The proposed DistArc loss encompasses three feature arrangement components: two angular and one radial, enforcing intra-class binding and inter-class separation in multi-radial arrangement, improving feature discriminability. Evaluation of HyperSpaceX framework for the novel representation utilizes a proposed predictive measure that accounts for both angular and radial elements, providing a more comprehensive assessment of model accuracy beyond standard metrics. Experiments across seven object classification and six face recognition datasets demonstrate state-of-the-art (SoTA) results obtained from HyperSpaceX, achieving up to a 20% performance improvement on large-scale object datasets in lower dimensions and up to 6% gain in higher dimensions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
