VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

• URL: http://arxiv.org/abs/2305.19688v1
• Date: Wed, 31 May 2023 09:31:54 GMT
• Title: VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
• Authors: Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen and Jan van Gemert
• Abstract summary: Third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges. Challenges focused on addressing the limitations of data availability in training deep learning models for computer vision tasks.
• Score: 13.085098213230568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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