Abstract
Deep Learning requires large amounts of data to train models that work well. In data-deficient settings, performance can be degraded. We investigate which Deep Learning methods benefit training models in a data-deficient setting, by organizing the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop series, featuring four editions of data-impaired challenges. These challenges address the problem of training deep learning models for computer vision tasks with limited data. Participants are limited to training models from scratch using a low number of training samples and are not allowed to use any form of transfer learning. We aim to stimulate the development of novel approaches that incorporate prior knowledge to improve the data efficiency of deep learning models. Successful challenge entries make use of large model ensembles that mix Transformers and CNNs, as well as heavy data augmentation. Novel prior knowledge-based methods contribute to success in some entries.
Abstract (translated)
深度学习需要大量的数据来训练表现良好的模型。在数据稀缺的情况下，性能会受到影响。我们通过组织“VIPriors：用于高效深度学习的视觉归纳先验”研讨会系列（共四届），探讨了哪些深度学习方法有助于在数据不足的情况下训练模型。这些挑战旨在解决使用有限的数据训练计算机视觉任务中深度学习模型的问题。参赛者被限制只能从零开始使用少量的训练样本进行建模，并且不允许任何形式的迁移学习。我们的目标是激发创新的方法，这些方法可以利用先验知识来提高深度学习模型在数据效率方面的能力。成功的挑战入围作品采用了大型模型集成（结合了Transformer和CNN），以及大量的数据增强技术。基于新颖先验知识的方法也在一些参赛作品的成功中起到了关键作用。
URL
https://arxiv.org/abs/2506.08612
