The significant advances in applications of text-to-image generation models have prompted
the demand of a post-hoc adaptation algorithms that can efficiently remove unwanted …

Industry is at the forefront of adopting new technologies, and the process followed by the
adoption has a significant impact on the economy and society. In this work, we focus on …

Recent research has revealed that many machine-learning models and the datasets they
are trained on suffer from various forms of bias, and a large number of different fairness …

With growing awareness of societal impact of artificial intelligence, fairness has become an
important aspect of machine learning algorithms. The issue is that human biases towards …

Machine Learning (ML) models are trained using historical data that may contain
stereotypes of the society (biases). These biases will be inherently learned by the ML …

Diffusion models have shown their effectiveness in generation tasks by well-approximating
the underlying probability distribution. However, diffusion models are known to suffer from …

Fairness has become an essential problem in many domains of Machine Learning (ML),
such as classification, natural language processing, and Generative Adversarial Networks …

Abstract Pre-trained Generative Adversarial Networks (GANs) can provide rich information
and make various downstream tasks beneficial. However, the training process of GANs …

Background: The early stage of defect prediction in the software development life cycle can
reduce testing effort and ensure the quality of software. Due to the lack of historical data …

Automated video summarization is a vision task that aims to generate concise summaries of
lengthy videos. Recent advancements in deep learning have led to highly performant video …