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Found 9 papers, 5 papers with code
Visual Style Prompting with Swapping Self-Attention
Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leakage.
Sequential Data Generation with Groupwise Diffusion Process
We present the Groupwise Diffusion Model (GDM), which divides data into multiple groups and diffuses one group at one time interval in the forward diffusion process.
Generator Knows What Discriminator Should Learn in Unconditional GANs
Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps.
Memory Efficient Patch-based Training for INR-based GANs
However, training existing approaches require a heavy computational cost proportional to the image resolution, since they compute an MLP operation for every (x, y) coordinate.
RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles
Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention between text and style.
Few-shot Compositional Font Generation with Dual Memory
By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples.
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings.
Deep Saliency with Encoded Low level Distance Map and High Level Features
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
