Zero-Interpolation Models: Bridging Modes with Nonlinear Latent Spaces
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P107Keywords:
Zero-interpolation models, nonlinear latent spaces, latent manifold learning, generative modeling, mode bridging, multimodal data, variational autoencoders, geodesic interpolation, latent space geometry, representation learning, non-Euclidean spaces, data-driven interpolation, smooth latent transitions, manifold-aware generation, deep generative models, topological data analysis, path planning in latent space, diffusion-based models, image-to-image translation, multimodal representationAbstract
Zero-interpolation models provide a fresh development in generative modeling as they allow one to negotiate complex, multimodal latent spaces without running into the common problems with mode collapse & also implausible transitions. When switching between different data modes, conventional interpolation methods especially linear algorithms have trouble typically generating more synthetic results that fail to reflect any other actual distribution within the training information. This work addresses the challenge by building paths respecting the inherent geometry of the latent space using a nonlinear, manifold-aware interpolation technique. These zero-interpolation models are designed to cover high-probability regions, therefore avoiding implausible samples and more faithfully reflecting the range seen in multimodal distributions. Our contributions begin with a theoretical framework that grounds zero-interpolation in Riemannian geometry, hence clarifying the shortcomings of present methods. We then provide a method based on learning latent structures to produce smooth, nonlinear trajectories over modes. Extensive empirical evaluations on synthetic & actual world datasets show that whilst greatly improving mode coverage and sample integrity, our models retain semantic consistency. We provide a case study in image synthesis to support our approach even further by showing how zero-interpolation helps more coherent transitions between different kinds of visual styles. Natural language processing also shows the ability of the model to generate grammatically accurate & contextually suitable interpolations between many other language elements. The findings show that, particularly in situations where mode variation & interpolation quality are too crucial, zero-interpolation is a reasonable path for improving the quality and reliability of generative models
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