Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …

Abstract 3D hand-object interaction data is scarce due to the hardware constraints in scaling
up the data collection process. In this paper we propose HOIDiffusion for generating realistic …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

Self-supervised learning has achieved remarkable success in acquiring high-quality
representations from unlabeled data. The widely adopted contrastive learning framework …

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have
emerged to address these issues by enabling a paradigm that relies on generative AI …

The performance of deep learning models is intrinsically tied to the quality volume and
relevance of their training data. Gathering ample data for production scenarios often …

This study leverages synthetic data as a validation set to reduce overfitting and ease the
selection of the best model in AI development. While synthetic data have been used for …

Continual Learning (CL) often relies on the availability of extensive annotated datasets, an
assumption that is unrealistically time-consuming and costly in practice. We explore a novel …

This paper tackles the emerging challenge of training generative models within a self-
consuming loop, wherein successive generations of models are recursively trained on …