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Found 8 papers, 1 papers with code
Variational Distribution Learning for Unsupervised Text-to-Image Generation
We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training.
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning
In AAM, the correlation between each patch feature and the synthetic image attribute is used as the importance weight for each patch.
FLAME: Free-form Language-based Motion Synthesis & Editing
FLAME involves a new transformer-based architecture we devise to better handle motion data, which is found to be crucial to manage variable-length motions and well attend to free-form text.
Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness
Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training.
Deep Quotient Manifold Modeling
One of the difficulties in modeling real-world data is their complex multi-manifold structure due to discrete features.
Manifold Learning and Alignment with Generative Adversarial Networks
We present a generative adversarial network (GAN) that conducts manifold learning and alignment (MLA): A task to learn the multi-manifold structure underlying data and to align those manifolds without any correspondence information.
Encoder-Powered Generative Adversarial Networks
We present an encoder-powered generative adversarial network (EncGAN) that is able to learn both the multi-manifold structure and the abstract features of data.
Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology
Exploiting the deep generative model's remarkable ability of learning the data-manifold structure, some recent researches proposed a geometric data interpolation method based on the geodesic curves on the learned data-manifold.
