VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
This addresses the problem of data scarcity in deep learning for researchers, but it is incremental as it builds on existing challenge formats.
The paper introduced the VIPriors challenges to tackle data-efficient deep learning by training models from scratch with reduced training samples and no pre-trained models, resulting in top solutions achieving significant performance increases through methods like data augmentation and novel architectures.
We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges. We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set. Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques. The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.