We introduce SynCLR a novel approach for learning visual representations exclusively from
synthetic images without any real data. We synthesize a large dataset of image captions …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

The power of DNNs relies heavily on the quantity and quality of training data. However,
collecting and annotating data on a large scale is often expensive and time-consuming. To …

Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …

Dataset distillation reduces the storage and computational consumption of training a
network by generating a small surrogate dataset that encapsulates rich information of the …

We present SynthCLIP, a novel framework for training CLIP models with entirely synthetic
text-image pairs, significantly departing from previous methods relying on real data …

Data augmentation is vital for object detection tasks that require expensive bounding box
annotations. Recent successes in diffusion models have inspired the use of diffusion-based …

Multipanel images, commonly seen as web screenshots, posters, etc., pervade our daily
lives. These images, characterized by their composition of multiple subfigures in distinct …

Recently, the growing capabilities of deep generative models have underscored their
potential in enhancing image classification accuracy. However, existing methods often …

Synthetic training data has gained prominence in numerous learning tasks and scenarios,
offering advantages such as dataset augmentation, generalization evaluation, and privacy …