  The third edition of the "VIPriors: Visual Inductive Priors for
Data-Efficient Deep Learning" workshop featured four data-impaired challenges,
focusing on addressing the limitations of data availability in training deep
learning models for computer vision tasks. The challenges comprised of four
distinct data-impaired tasks, where participants were required to train models
from scratch using a reduced number of training samples. The primary objective
was to encourage novel approaches that incorporate relevant inductive biases to
enhance the data efficiency of deep learning models. To foster creativity and
exploration, participants were strictly prohibited from utilizing pre-trained
checkpoints and other transfer learning techniques. Significant advancements
were made compared to the provided baselines, where winning solutions surpassed
the baselines by a considerable margin in all four tasks. These achievements
were primarily attributed to the effective utilization of extensive data
augmentation policies, model ensembling techniques, and the implementation of
data-efficient training methods, including self-supervised representation
learning. This report highlights the key aspects of the challenges and their
outcomes.
