Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Large-scale text-to-image generative models have shown their remarkable ability to
synthesize diverse, high-quality images. However, directly applying these models for real …

Recent text-to-image diffusion models are able to generate convincing results of
unprecedented quality. However, it is nearly impossible to control the shapes of different …

Recent text-to-image generation methods provide a simple yet exciting conversion capability
between text and image domains. While these methods have incrementally improved the …

Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired
image-to-image translation (I2I). However, we notice that existing methods totally ignore the …

We observe that despite their hierarchical convolutional nature, the synthesis process of
typical generative adversarial networks depends on absolute pixel coordinates in an …

Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

One of the major challenges in training text-to-image generation models is the need of a
large number of high-quality text-image pairs. While image samples are often easily …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …