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Found 7 papers, 1 papers with code
Cross-Domain Cascaded Deep Translation
We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features.
Noise-Free Score Distillation
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains.
Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons
We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose.
Multi-level Latent Space Structuring for Generative Control
Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity.
Cross-Domain Cascaded Deep Feature Translation
Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones.
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks.
DiDA: Disentangled Synthesis for Domain Adaptation
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.
