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Found 20 papers, 6 papers with code
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models
The rapid advancement in image generation models has predominantly been driven by diffusion models, which have demonstrated unparalleled success in generating high-fidelity, diverse images from textual prompts.
MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models
Addressing intersectional bias is crucial because it amplifies the negative effects of discrimination based on race, gender, and other identities.
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models
Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining.
CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models
Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects.
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
Our extensive experiments show that our method achieves highly disentangled edits, outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text.
3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes
Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to represent and generate 3D shapes, as well as a vast number of use cases.
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs
We evaluate our framework with qualitative and quantitative experiments and show that our method finds more diverse and disentangled directions.
Discovering Multiple and Diverse Directions for Cognitive Image Properties
While previous work has focused on discovering a single direction that performs a desired editing operation such as zoom-in, limited work has been done on the discovery of multiple and diverse directions that can achieve the desired edit.
FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations
Our experiments show that our method successfully debiases the GAN model within a few minutes without compromising the quality of the generated images.
Text and Image Guided 3D Avatar Generation and Manipulation
In this work, we propose a novel 3D manipulation method that can manipulate both the shape and texture of the model using text or image-based prompts such as 'a young face' or 'a surprised face'.
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data.
Exploring Latent Dimensions of Crowd-sourced Creativity
We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative.
Controlled Cue Generation for Play Scripts
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues.
Graph2Pix: A Graph-Based Image to Image Translation Framework
In this paper, we propose a graph-based image-to-image translation framework for generating images.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs).
A Structural Smoothing Framework For Robust Graph Comparison
In this paper, we propose a general smoothing framework for graph kernels by taking \textit{structural similarity} into account, and apply it to derive smoothed variants of popular graph kernels.
Deep Graph Kernels
In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning.
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WordRank: Learning Word Embeddings via Robust Ranking
Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.
The Structurally Smoothed Graphlet Kernel
This vector representation can be used in a variety of applications, such as, for computing similarity between graphs.
