Current deep networks are very data-hungry and benefit from training on large-scale
datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data …

We propose an experimental method for measuring bias in face recognition systems.
Existing methods to measure bias depend on benchmark datasets that are collected in the …

Although recent developments in deep learning have brought several tasks closer to human
performance, there is still a significant gap between human and machine performance in …

We present a novel approach for generating minority samples that live on low-density
regions of a data manifold. Our framework is built upon diffusion models, leveraging the …

We explore the problem of generating minority samples using diffusion models. The minority
samples are instances that lie on low-density regions of a data manifold. Generating a …

We investigate the generation of minority samples using pretrained text-to-image (T2I) latent
diffusion models. Minority instances, in the context of T2I generation, can be defined as ones …

In many image classification applications, the number of labeled training images is limited,
which leads to model overfitting. To mitigate the lack of training data, deep generative …

Data scarcity is a very common real-world problem that poses a major challenge to data-
driven analytics. Although a lot of data-balancing approaches have been proposed to …

Recent work demonstrates that images from various chest X-ray datasets contain visual
features that are strongly correlated with protected demographic attributes like race and …

In today's age of big data, data sharing among companies, customers, and researchers has
become a critical activity that drives advancements across industry and academia. In these …