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Found 12 papers, 4 papers with code
Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models
With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content.
A Unified and Interpretable Emotion Representation and Expression Generation
However, emotions are often compound, e. g. happily surprised, and can be mapped to the action units (AUs) used for expressing emotions, and trivially to the canonical ones.
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques.
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention
Specifically, we insert parallel attention matrices to each cross-attention module in the denoising network, which attends to features extracted from reference images by an identity encoder.
Lego: Learning to Disentangle and Invert Concepts Beyond Object Appearance in Text-to-Image Diffusion Models
To enable customized content creation based on a few example images of a concept, methods such as Textual Inversion and DreamBooth invert the desired concept and enable synthesizing it in new scenes.
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e. g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses.
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
By training a weak GAN and using its generated output image parallel to the regular GAN, the Vanishing Twin training improves semi-supervised image classification where image similarity can hurt classification tasks.
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
We propose a modification to the traditional training of GANs that allows for improved multi-class classification in similar classes of images in a semi-supervised learning framework.
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19.
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively.
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Using a fine-tuning data of 115 patients from the target domain, dice score coefficient of 0. 85 and 0. 84 are achieved for segmentation of whole gland and transition zone, respectively, in the target domain.
