Motivated by concerns that large-scale diffusion models can produce undesirable output
such as sexually explicit content or copyrighted artistic styles, we study erasure of specific …

Text-to-image models suffer from various safety issues that may limit their suitability for
deployment. Previous methods have separately addressed individual issues of bias, copyright, …

We present a method to create interpretable concept sliders that enable precise control over
attributes in image generations from diffusion models. Our approach identifies a low-rank …

Concept erasure in language models has traditionally lacked a comprehensive evaluation
framework, leading to incomplete assessments of effectiveness of erasure methods. We …

In this work, we propose an end-to-end trainable model of Generative Adversarial Networks
(GAN) which is engineered to hide audio data in images. Due to the non-stationary property …

The noise correction and image enhancements are done at the Data Processing Generation
System (DPGS) at the National Remote Sensing Center (NRSC) satellite data production …

We describe a new training methodology to train Denoising Diffusion Probabilistic Models (DDPM)
for faster convergence speeds. DDPMs have achieved high-quality image synthesis …

This paper implements a deep learning-based Convolutional Neural Network (CNN) with
adversarial training and infused pixel information to classify multi-spectral data into 4 LULC …

Art is an artistic method of using digital technologies as a part of the generative or creative
process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for …

We explore the question: "How much prior art knowledge is needed to create art?" To
investigate this, we propose a text-to-image generation model trained without access to art-related …