A survey on data augmentation for text classification

M Bayer, MA Kaufhold, C Reuter - ACM Computing Surveys, 2022 - dl.acm.org
Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

A comprehensive survey on data augmentation

Z Wang, P Wang, K Liu, P Wang, Y Fu, CT Lu… - ar** language-image pre-training for unified vision-language understanding and generation
J Li, D Li, C **ong, S Hoi - International conference on …, 2022 - proceedings.mlr.press
Abstract Vision-Language Pre-training (VLP) has advanced the performance for many vision-
language tasks. However, most existing pre-trained models only excel in either …

A survey of data augmentation approaches for NLP

SY Feng, V Gangal, J Wei, S Chandar… - arxiv preprint arxiv …, 2021 - arxiv.org
Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Scaling laws of synthetic images for model training... for now

L Fan, K Chen, D Krishnan, D Katabi… - Proceedings of the …, 2024 - openaccess.thecvf.com
Recent significant advances in text-to-image models unlock the possibility of training vision
systems using synthetic images potentially overcoming the difficulty of collecting curated …

Leveraging large language models for multiple choice question answering

J Robinson, CM Rytting, D Wingate - arxiv preprint arxiv:2210.12353, 2022 - arxiv.org
While large language models (LLMs) like GPT-3 have achieved impressive results on
multiple choice question answering (MCQA) tasks in the zero, one, and few-shot settings …

Learning vision from models rivals learning vision from data

Y Tian, L Fan, K Chen, D Katabi… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …