The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation
Few-shot image generation is a challenging task since it aims to generate diverse new images for an unseen category with only a few images. Existing methods suffer from the trade-off between the quality and diversity of generated images. To tackle this problem, we propose Hyperbolic Attribute Editing~(HAE), a simple yet effective method. Unlike other methods that work in Euclidean space, HAE captures the hierarchy among images using data from seen categories in hyperbolic space. Given a well-trained HAE, images of unseen categories can be generated by moving the latent code of a given image toward any meaningful directions in the Poincar\'e disk with a fixing radius. Most importantly, the hyperbolic space allows us to control the semantic diversity of the generated images by setting different radii in the disk. Extensive experiments and visualizations demonstrate that HAE is capable of not only generating images with promising quality and diversity using limited data but achieving a highly controllable and interpretable editing process.
Code (1)
Tasks
AttributeDiversityImage GenerationSimilar Papers 제목 키워드 기반
Consistent Spectral Clustering in Hyperbolic Spaces
Clustering, as an unsupervised technique, plays a pivotal role in various data analysis applications. Among clustering algorithms, Spectral Clustering on Euclidean Spaces has been extensively studied. However, with the r…
ClusteringCombined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning
Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data points. Hyperbolic space is attractive for DML since it can represent ric…
Semantic SimilarityMetric LearningHyperbolic Diffusion Recommender Model
Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental st…
modelRecommendation SystemsModel Editing with Graph-Based External Memory
Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated parametric knowledge. Although post-training …
graph constructionmodelModel EditingClipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers
Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outp…
Adversarial RobustnessGeneral ClassificationOut-of-Distribution Detection