HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions
<|reference_start|>HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions: 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.<|reference_end|>
@article{chiranjeev2024hyperspacex:,
    title={HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions},
    author={Chiranjeev Chiranjeev, Muskan Dosi, Kartik Thakral, Mayank Vatsa and
  Richa Singh},
    journal={arXiv preprint arXiv:2408.02494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02494},
    primaryClass={cs.CV}
}