Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

协调几何与不确定性：结合超球面的扩散模型

Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres

针对标准扩散模型适配欧氏空间、丢失超球面数据角度几何细节的问题，提出HyperSphereDiff模型，用方向噪声对齐超球面结构，经理论与多数据集实验验证，可提升生成准确性与几何感知能力。

HEART：通过扩散模型中的Kent表示遍历实现超球面嵌入对齐

HEART: Hyperspherical Embedding Alignment via Kent-Representation Traversal in Diffusion Models

本文提出HEART框架，通过超球面几何对齐实现图像生成中的直观精确编辑，无需微调或优化，提升控制精度与稳定性。

Deep Hyperspherical Learning

Convolution as inner product has been the founding basis of convolutional neural networks (CNNs) and the key to end-to-end visual representation learning. Benefiting from deeper architectures, recent CNNs have demonstrated increasingly strong representation abilities. Despite such improvement, the increased depth and larger parameter space have also led to challenges in properly training a network. In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres. We introduce SphereNet, deep hypersphe…

Hyperspherical Consistency Regularization

Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning with a shared feature encoder becomes a common scheme. Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier. In this work, we systematically explore the relationship between self-supervised learning and supervised learning, and study how self-super…

用于点云补全的超球面嵌入

Hyperspherical Embedding for Point Cloud Completion

本文针对点云补全中特征嵌入分布稀疏、泛化不佳的问题，提出将编码器嵌入归一化到单位超球面并仅优化方向的模块，实现更稳定训练与紧凑分布，并在单任务和多任务实验中取得一致提升。

Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors

Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision. Yet these networks struggle when classes are very similar to each other (fine-grain classification) and currently have no way of taking into account prior knowledge (through the use of tabular data). Using a spherical latent space to encode prototypes, we can achieve few-shot fine-grain classification by maximally separating the classes while incorporating domain knowledge as informative priors. We describe how to construct a hypersphere of prototypes that embed a-priori domain information, and…