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On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Diane Wagner · Fabio Ferreira · Danny Stoll · Robin Tibor Schirrmeister · Samuel Gabriel Müller · Frank Hutter
Event URL: https://openreview.net/forum?id=oBmAN382UL »

Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks. However, the rapid pace of advancements in this area comes at a price: training pipelines vary significantly across papers, which presents a potentially crucial confounding factor. Here, we show that, indeed, the choice of hyperparameters and data augmentation strategies can have a dramatic impact on performance. To shed light on these neglected factors and help maximize the power of SSL, we hyperparameterize these components and optimize them with Bayesian optimization, showing improvements across multiple datasets for the SimSiam SSL approach. Realizing the importance of data augmentations for SSL, we also introduce a new automated data augmentation algorithm, GroupAugment, which considers groups of augmentations and optimizes the sampling across groups. In contrast to algorithms designed for supervised learning, GroupAugment achieved consistently high linear evaluation accuracy across all datasets we considered. Overall, our results indicate the underestimated role of data augmentation for SSL.

Author Information

Diane Wagner (University of Freiburg)
Fabio Ferreira (University of Freiburg)
Danny Stoll (University of Freiburg)
Robin Tibor Schirrmeister (Translational Neurotechnogy Lab, University Medical Center Freiburg)
Samuel Gabriel Müller (Universität Freiburg)
Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence)
Frank Hutter

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he has been a faculty member since 2013. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on automated machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

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