Search Results for author:
Found 7 papers, 7 papers with code
Evaluating Data Attribution for Text-to-Image Models
The problem of data attribution in such models -- which of the images in the training set are most responsible for the appearance of a given generated image -- is a difficult yet important one.
Ablating Concepts in Text-to-Image Diffusion Models
To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i. e., preventing the generation of a target concept.
Content-Based Search for Deep Generative Models
To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query.
Ranked #1 on
Model Description Based Search
on Generative Models
Contrastive Learning
Image and Sketch based Model Retrieval
+4
Rewriting Geometric Rules of a GAN
Our method allows a user to create a model that synthesizes endless objects with defined geometric changes, enabling the creation of a new generative model without the burden of curating a large-scale dataset.
Sketch Your Own GAN
In particular, we change the weights of an original GAN model according to user sketches.
CNN-generated images are surprisingly easy to spot... for now
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.
Detecting Photoshopped Faces by Scripting Photoshop
Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop.
